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readFile \"./analytics.js\" | md5 }}"}]]}]}] c:I{"id":4896,"chunks":["550:static/chunks/925f3d25-4b71ba0de552bdc4.js","896:static/chunks/896-8c3b49c69396a8f0.js","126:static/chunks/app/resources/page-3de523ef98639362.js"],"name":"","async":false} d:I{"id":1254,"chunks":["550:static/chunks/925f3d25-4b71ba0de552bdc4.js","896:static/chunks/896-8c3b49c69396a8f0.js","126:static/chunks/app/resources/page-3de523ef98639362.js"],"name":"","async":false} e:T630,Open-source AI is evolving rapidly, driving significant innovation in generative AI. With accessible research and platforms like GitHub and Hugging Face, the community has launched groundbreaking projects with impressive results.
We initiated research on Open Source AI in Jan’24, here’s a summary of our piece:
The space has evolved significantly in just a few months, looking completely different from our Jan’24 piece. Open-source AI models have closed the gap with their closed-source counterparts in several benchmarks. Investor interest has surged, with over 30 deals in 2024 alone. Meanwhile, startup activity has evolved from building fundamental tools to driving downstream innovation in model training and monitoring use cases.
Here’s a fast forward to mid-2024 with the latest developments in the open-source AI space.
f:T86e,
Significant surge in startups building Open Source AI products
The number of participants in open-source AI has surged since our last coverage of this space, with new players like Neum AI, Patronus AI entering the field and established players like Vian AI making open source toolkits available for their users.
Dev tools remain hot; training and monitoring tools seeing increased competition
A majority of startups are still focusing on developer tools for generative AI, which are essential for building, deploying, and managing applications. However, there has been an increase in startup activity around model training and monitoring use cases, suggesting a potential shift toward models fine-tuned on niche data and enhanced AI governance. In open-source models, winners are starting to emerge, with fewer new models being developed and greater emphasis on improved, more efficient versions from companies like Mistral and Meta.
OS development is bridging has bridged the gap with closed source solutions
41% of enterprise users prefer Open Source for Gen AI needs. Open source enables affordable and accessible research, fosters innovation from diverse creators, and operates with fewer legal constraints. Thus, it is being recognized as a much more prominent area for development, with the gap between closed-source and open-source offerings narrowing significantly.
10:T606,
High funding momentum in open-source AI, startups graduating to growth-stage deals
The sector has seen 60+ deals in the past two years, totalling over $13 billion in funding. More than 45% of these deals are Series A+, indicating a strong focus on growth-stage investments.
Model training and developer tools are the most heavily funded segments in open-source AI (outside of Mistral and Databricks), accounting for 60% of the total funding in the sector.
NVIDIA continues to be a strategic investor, participating in eight deals (including Scale AI, Mistral AI, Together AI) within this space.
11:T7bf,
Open Source AI models had already been catching up with their closed source counterparts towards the end of 2023. The benchmark gap between open and closed models is now narrower than ever – with Meta Llama, Mistral almost the same as GPT 4o in MMLU. Llama 3.1, launched mere weeks ago, is at par with GPT’s latest model in MMLU benchmark.
The Open-LLM-Leaderboard, launched by Hugging Face in June 2024, ranks open-source LLMs using new benchmarks focused on complex tasks, addressing limitations of existing evaluations (such as Elo and MMLU) as models plateaued in performance.
Qwen is an open-source LLM developed by Alibaba Cloud, pretrained on a large volume of data, including web texts, books, and codes.
New leaders are emerging amidst robust competition in Open Source LLMs
Falcon and Bloom were leaders in Huggingface Traction in December 2023. Over the past six months, the landscape has shifted significantly with new competitors emerging. Qwen saw the highest downloads in June’24; Llama and Phi have also seen substantial traction.
12:T59c,
GitHub stars (similar to a “follow” on social media) are a direct indicator of a project’s popularity on GitHub. AutoGPT and ModularML’s Mojo led GitHub traction in 2023 – several repositories have gained significant momentum since then.
LeRobot provides models, datasets, and tools for real-world robotics in PyTorch, aiming to make robotics more accessible. It features state-of-the-art approaches in imitation and reinforcement learning, offering pretrained models, human-collected datasets, and simulation environments.
MindsDB, backed by NVIDIA, is a platform for building AI models using enterprise data. It allows users to deploy, serve, and fine-tune models in real-time by integrating with various data sources and AI/ML frameworks. MindsDB simplifies the connection between data sources and AI/ML tools, automating workflows to create customized AI systems.
13:T91e,
Interested in learning more about startups building in open source AI? We have curated a list of 75+ amazing Open Source AI startups, that are seeing great traction on alternative datasets like Github, Employee counts, Website visits, Job openings, and more. Download the list here.
14:T4ae,We love market maps! They provide a structured way to track and understand a sector. We've been monitoring Generative AI closely (who isn't!), and maintain a searchable collection of market maps internally. We're making this resource public for everyone to access and collaborate.
Introducing the Gen AI Market Map Library – a live collection of over 100+ awesome market maps from leading VCs like Sequoia, a16z, BVP.
Streamline your Gen AI research!
Have a market map to add? Share it with us here!
15:T710,Generative AI is streamlining game development and enhancing in-game experiences for players.
Game building is typically resource-intensive in nature. Gen AI can significantly reduce this by generating 3D assets, virtual environments, in-game conversations, game testing bots and more. Startups like Luma AI, Common Sense Machines are enabling the creation of 3D assets from text / image prompts. Gen AI tools like Inworld AI are already gaining traction amongst developers and user-generated content (UGC) creators – Inworld AI has been used to create mods for popular games like Skyrim and Grand Theft Auto V.
Large game developers are taking notice – Microsoft's announced a collaboration with Inworld AI to design a multi-platform AI toolset, including an AI design copilot for scripts and dialogue, and an AI character engine that can be integrated into games and used to dynamically generate stories, quests, and dialogue.
However, major publishers have differing opinions on AI-generated gaming content. While Steam removed certain games containing AI-generated content (citing copyright risks), Epic Games has welcomed games containing AI-generated content.
The gaming toolscape is buzzing with activity - innovation across different stages of game development, strong consumer interest, and fundraising momentum. Read on for an overview of the market.
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Cost-effective, time-efficient production: The game development process is time-intensive and costly, and often involves expensive stock assets from stores like Unity’s. Gen AI can create assets with just a prompt, cutting down on both time and resources required. Luma AI allows developers to create basic 3D characters to complete interactive 3D scenes by entering text prompts, even on their mobile phone. Scenario is helping game studios like LLC create thousands of assets monthly for 10% of the cost, compared to their earlier capacity of only a few assets per month. Gen AI can enable individual developers and smaller studios to be creative as well as cost- and time-efficient in production.
Better NPC conversations, responsive game environments: Gen AI can modify game environments on the fly to adapt to player choices and generate NPC barks (dialogues from non-player characters) based on gamer activity. Inworld’s AI Engine can respond to players' actions and decisions with dynamic game states, environments, and AI agents – transforming standard gameplay into a bespoke experience for each player, each time they play.
User-generated content (UGC) creation becoming more accessible through Gen AI: User-generated content (UGC) and modding (players modifying aspects of a game) have been integral to the gaming fandom, and led to popular games like Dota 2 (Warcraft III) and Counterstrike (Half-Life). Players are able to create in-game assets (like custom maps, items, skins) or entirely modified versions of the game that they can share, or sell to other players – making games larger and more personalized. Companies have often incentivised players to create UGC – Epic announced that they’d give 40% of Fortnite’s net revenue back to its UGC creators through engagement payouts.
Gen AI enables anyone who’s interested to create game content & assets without needing special design / coding skills, thus reducing the skill barrier. This will make UGC creation more accessible and abundant. Roblox, one of the largest platforms for UGC creators, has announced an AI Assistant which lets creators build virtual assets and write code with Gen AI.
Multiple game developer use-cases for Gen AI
Alongside assisting developers, AI is also being used as a gamemaster in character-based games like AI Dungeon, The Fortuna, and Gandalf by Lakera, fully directing the game's progression and interactions.
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AI innovation is largely centred around game developer use-cases, especially asset creation
AI innovation in gaming is heavily focused on developer tooling, especially around tools that automate the creation of game assets. Asset creation is traditionally tedious and labor-intensive, and purchasing assets from stores is expensive. Tools like Luma AI, Hypothetic can generate detailed 3D characters, objects, textures, and entire visual worlds from simple text prompts. AI asset creation tools solve for one of the biggest struggles in game dev – having to compromise between creativity and efficiency.
Like other Gen AI applications, the Copilot model is being explored in Gaming as well by large game dev platforms
The Copilot model, already popular in coding and copywriting, is gaining traction in gaming. Industry incumbents like Unity and Roblox are coming up with AI Assistants that support multiple stages of game development. AI copilots that are capable of creating complete games from text prompts are coming up — crafting everything from immersive worlds to characters and narratives. Some startups offering AI copilots for game development are Rosebud AI, Series AI, and Astrocade.
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Robust funding landscape with 30+ deals for emerging startups in 2022-24
Despite a 72% decline in overall gaming investment in 2023, AI gaming startups have attracted several deals, primarily Seed and Series A rounds. Both VCs & strategic investors are actively participating in the market:
Inworld AI is the highest funded startup in AI x Gaming
Inworld AI’s $50M deal in 2023 made it the highest funded startup in the space. This also put Inworld AI at "halficorn" status, with a post-money valuation of $500 million.
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The game developer community builds on game engines like Unity, Unreal Engine, thus, several dev tools are listed on the asset stores of these engines. Reviews on these stores serve as a good measure of a tool’s popularity.
Both the top popular choices are serving towards better in-game NPC conversations.
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Luma AI uses AI to enable users to generate realistic 3D environments from text prompts and 2D photographs. The process is entirely doable on an iPhone via the Luma AI App. This tech has attracted interest from a broad audience including game developers, architects, VFX professionals and video content creators. Luma AI is backed by NVIDIA.
Common Sense Machines (CSM.ai) offers end-to-end 3D worlds generation from generating 3D assets, to animation, to stylization. Users can generate 3D models from text prompts, sketches or 2D images.
Inworld’s Character Engine uses AI to generate NPCs whose personalities, thoughts, memories, and behaviours are designed to mimic the social nature of human interaction. Inworld has worked with multiple AAA brands including Disney, NetEase Games and Niantic to build AI-driven NPC experiences. Inworld is also Microsoft’s official partner for a multiyear project to enhance Xbox development tools, including an "AI design copilot" for developers to create AI-driven characters, narratives, and quests.
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Amidst the flood of innovative AI startups emerging daily, many of the most valuable ventures including OpenAI started in stealth mode. Our report identifies serial entrepreneurs with AI experience building stealth startups.
We have picked out the profiles by extensively analyzing their prior work experiences and academic backgrounds, drawing from a vast dataset of millions of founders and talent profiles.
Key insights in the report:
Download the report to explore these promising founders, their core experience, and more such insights!
1d:T574,Generative AI is poised to transform how educational content is developed, personalized, and consumed.
For instructors, AI can generate lesson plans, course content, assignments / quizzes, and tailored instruction to meet a wide array of learner needs. Gradescope AI is helping teachers grade a class of 250+ in 15 minutes. Edapp’s AI-Create saves 81% instructors ~2 hours of time while creating course content. For learners, AI are capable of generating interactive and adaptive coursework that can challenge them at their individual pace and skill levels. Personalised AI tutors like Flint can provide instant feedback and support, filling in gaps in understanding – a feature that has led to clear increase in engagement at certain K12 institutions.
Gen AI can also empower self-learners with curated learning content based on prompts, summarized research papers for quicker understanding, and translated educational material to unlock global content.
Gen AI in Edtech can democratize learning on an unprecedented scale. However, it poses a significant challenge to upholding academic integrity – a struggle that is compelling institutions and instructors to reimagine assignments.
Read the full report for an overview of the AI Edtech market, funding landscape and emerging tools.
1e:T617,Join us for an interactive discussion with Sagar Chandna, Senior Partner and CTO at RunwayFBU.
“Data-driven” has become a catch-all term for vastly different approaches to tooling & data across the private investing activity stack.
Hear from Sagar as he parses through the noise, with a practical perspective on how RunwayFBU embedded data and tooling across their investment process - how they started, key challenges, learnings, and the value they realized.
Key discussion points:
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If you are looking for a curated list of the best tools you will ever need to deal with the complexities of Venture Capital and Private Equity, keep reading! We have shortlisted the best tools to help you through deal sourcing, due diligence, portfolio management, and other complex and time-consuming processes. We will deep-dive into their functionalities, benefits, and how they can help you invest better. From core stack tools to general productivity and collaboration, these tools help you become data-driven and get insights into market trends, performance benchmarks, etc. You can also automate your workflows to save time and make investments with a high ROI.
All these different data tools can be categorized into three broad categories based on their use cases across the deal flow cycle.
The core stack consists of data and tools that drive the core fund operations and is built for private investors. Here are some key core stack tools (with examples):
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These platforms streamline relationship management with entrepreneurs, investors, and other stakeholders while organizing deal flow from various sources.
Leading Tools
These tools aid in sourcing potential investment opportunities and conducting initial screening processes to identify promising ventures. To recognize a good sourcing platform, check if it provides the following three things:
Here are a few reliable sourcing and screening platforms and tools:
Sourcing Platforms
Sourcing Tools
Dedicated to tracking and analyzing investment performance, these tools enable VC firms to manage their portfolios effectively.
The biggest problem with portfolio management is managing unstructured company updates. As an investor, you receive company updates in multiple formats including Excel sheets, emails, PDFs, Notion documents, etc. With no standardization, it can be tough to manage both — tracking every company in your portfolio and tracking potential companies in depth. Therefore, most VCs either limit the number of companies they track to only the most important ones or spend long hours manually compiling these reports.
An ideal portfolio management platform will scan and compile all these documents and let you track every metric for all the companies in your portfolio.
Some of the best Portfolio Management tools in the industry include:
These platforms handle fund administration tasks such as compliance, accounting, and reporting to investors.
Job Boards and Product Portals
Job boards help VCs find and recruit top talent for their firms. Product portals allow them to discover promising startups, understand their offerings, and potentially initiate investment conversations.
Network and Community Management Tools
These platforms allow VC firms to build relationships, share insights, and exchange ideas with entrepreneurs, co-investors, and industry experts. These tools help VCs expand their professional networks, access valuable resources, and stay updated on industry developments, ultimately enhancing their ability to make strategic investment decisions and drive portfolio success.
General tools include regular business tools that enhance productivity, collaboration, data management, and marketing efforts for investors.
Note-taking and Knowledge Management:
Productivity and Collaboration:
Data Rooms:
Marketing Stack:
HR Stack:
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The Digital Health 30 Report is back!
Created as a one-stop resource for investors seeking exposure to the Digital Health industry, this third edition of our blockbuster report covers a curated set of 30 rapidly evolving Digital Health startups across different stages of funding and growth in their lifecycle.
Key Findings:
This report provides detailed insights into each company’s performance based on alternative data metrics like website traffic growth, employee growth, product ratings, and many others available on the Synaptic platform.
Download the complete report now!
In 2023, AI startups raised $42.5B across 2,500 equity rounds. With over 250 AI deals in Jan and Feb ‘24, investments are expected to rise further as we move further in 2024.
AI Innovators is a curated list of 50 high-growth private AI companies that stand at the forefront of innovation within AI infrastructure and AI application space, offering both cross-functional and function-specific solutions.
Key Findings:
The report further dives deeper into the top performers across different funding rounds based on alternative data metrics such as web traffic and workforce size.
Download the complete report today!
Generative AI has started unlocking real value in Fintech. Leading firms are already deploying the technology to drive efficiencies - Brex's AI assistant for expense management saves approximately 4,000 hours of manual effort annually. Financial giants like Bloomberg and JPMorgan are developing proprietary generative AI tools – BloombergGPT currently focuses on improving financial NLP tasks like sentiment analysis, named entity recognition, news classification, and question answering, while JP Morgan’s IndexGPT aids investors in analyzing securities.
AI is not new in Fintech, nearly 90% of fintech firms were leveraging AI/ML to some degree in 2020. Generative AI however, provides a new path to enhance productivity on the still tedious, documentation-heavy financial workflows like billing & invoicing, or compliance reviews. For instance, a large US distribution company is processing 76% of invoices without human intervention using Vic.ai, averaging 36 seconds per invoice instead of 2.3 minutes.
Despite a general downturn in global fintech investment, AI-focused fintech managed to attract over $12.1 billion in funding in 2023. Read on for an overview of the space.
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The fintech market serves a wide range of customers – from individuals to companies to governments – across various use cases. Fintech has long leveraged AI/ML to improve areas like algorithmic trading and risk assessment. Large language models (LLMs) introduce a fresh wave of innovation, especially in automating repetitive, language-focused tasks.
Finance back-office tasks involve significant manual handling of documents, bills, invoices - well suited for LLM-driven automation
The workload of a finance team involves handling a large volume of documents, including financial data, transactions, compliance documents, bills, and invoices. LLMs excel at parsing, understanding, and managing such vast amounts of textual data. They can automate the extraction of important details from financial documents, categorize and summarize transaction information, and compile detailed reports from various data points. LLMs also enhance customer interaction and query resolution, with AI chatbots and knowledge assistants interpreting and responding to customer queries – freeing up time for fintech professionals to focus on more critical tasks.
LLM adoption is easier in applications not dealing with sensitive financial data directly
Like the healthcare sector, the fintech industry handles private and sensitive financial details, necessitating regulatory oversight and compliance. Use cases that don't directly involve such sensitive information – such as personal finance management, investment research, back-office workflows like expense tracking, document processing – are more straightforward for LLM adoption, as they may require fewer regulations.
The first wave of LLM adoption is being seen in the following use cases:
CFO Tooling (accounting, bookkeeping, expense management ): Generative AI can enhance financial operations by automating tasks such as accounting, transaction coding, expense tracking, and the creation of receipts and memos. It can also understand patterns and identify exceptions, provide accurate forecasts, and offer personalized insights. This reduces the manual workload for finance professionals. A number of players such as Basis, Arkifi, Vic.ai are offering a “copilot model” for finance teams.
Gen AI can process, summarize and extract relevant data from a bulk of financial documents (e.g. annual reports, financial statements, and earnings calls), which can thereafter be ingested into databases – significantly reducing human workload in doing so. Tools like Sensible.so, Rossum.ai, Attri.ai are using Gen AI to extract data from financial documents and expedite document processing.
Onboarding, Risk & Compliance (fraud, & underwriting, KYC/KYB): Onboarding and AML processes involve a hefty amount of paperwork. Gen AI can automate compliance reviews, requests for missing docs, and sanction alerts. For instance, Greenlite utilizes generative AI to automate compliance reviews for customer onboarding. Coris AI, a Risk API for fintech companies, uses GPT-4 to automatically determine a merchant’s classification codes, which are essential for monitoring risk and compliance.
LLMs can add an additional layer of checks by leveraging language-based sources that were previously untapped or manually analyzed. For instance, Flagright employs GPT-4 to monitor merchant profiles using public platforms like Crunchbase and LinkedIn – assessing a merchant's reputation, clientele, and financial health. Additionally, Flagright uses GPT to create Suspicious Activity Reports (SARs) based on flagged activity and supports risk analysts with an AI Narrative Writer, streamlining routine documentation and communication tasks.
Investment Research: Financial research is being enhanced with Gen AI, allowing investors to carry out research on stocks, public and private markets on a chat interface. StockGPT, an AI-powered financial research tool, lets investors search through earnings releases, financial reports, and other fundamental information for all S&P 500 and Nasdaq companies. FinChat offers financial data on 100,000+ global public companies that users can search from, along with a stock screener, data visualizations, competitor comparisons, and portfolio dashboarding.
Personal Finance: Tools for personal wealth management and financial advice are using Gen AI to give more personalised communication and advice. For example, Wally uses GPT to summarize expenses, provide financial advice based on personal goals, and provides insights into financial habits. UStreet is an AI wealth advisor that creates personalised retirement plans, offers portfolio reviews and tax saving advice.
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Incumbent firms are building proprietary AI models for their teams
Fine tuning models on relevant financial data is critical to creating relevant workflows with LLMs. This puts established firms at an advantage – BloombergGPT, JPMorgan’s IndexGPT, Intuit’s GenOS, Morgan Stanley’s AI Assistant have all been built by incumbent firms with the data and infrastructure needed for training proprietary models.
Established startups are actively enhancing their offerings using Gen AI
Established firms that have already built a customer base are improving their products with Gen AI. Companies offering CFO tooling are integrating AI Assistants into their products, enabling users to interact using natural language for financial data insights – prime examples are Vise Intelligence and PilotGPT. Investment research is being boosted with Gen AI – Alpha by Public.com provides real-time investing context and proactive alerts about market movement to investors.
New entrants are innovating with CFO tooling, compliance and research tools
New entrants in fintech are primarily developing tools that streamline tasks and enhance efficiency for finance professionals, freeing up their time for higher-value work. These tools automate routine tasks like accounting, bookkeeping, and managing expenses, as well as compliance-related activities such as detecting fraud, underwriting, and customer onboarding.
AI innovation will have to navigate fintech’s high bar for data privacy and accuracy
The bar for data quality, privacy and accuracy in financial services is among the highest in the business world. Gen AI adoption, therefore, is currently taking place in selective areas which do not directly impact major financial decisions and therefore need not navigate strict regulatory processes.
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Despite the funding for the global fintech market hitting a five-year low in 2023, investor interest in AI within fintech has remained strong.
More than 75% of emerging startups in the space have raised money in the last 2 years
Funding for emerging players has been robust in 2022-24. There have been 30+ deals, albeit small ones, across the sector for AI-native players.
Some notable deals:
Several speculative rounds, averaging around ~5M, on emerging players in the space
Investors are being cautiously optimistic about AI in fintech, making speculative bets on emerging players. Although fintech is a high-growth area, its regulatory landscape and the nascent state of AI integration create a risk profile that favors smaller initial investments. This allows startups to prove viability and secure regulatory approval before attracting larger funding rounds.
Established startups that embedded Gen AI as a fundamental aspect of their core products secured the largest funding deals in 2022-24
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Interest in AI Fintech has been commensurate with the broader AI trend of initial hype transitioning to more value-driven engagement – similar to the AI customer support tools sector or the AI healthcare tools sector. After a peak in Q1’23, website traffic across the sector has stabilized.
B2B tools have seen the most QoQ growth in website traction; B2C tools have seen great volume but deceleration
Consumer finance tools like Magnifi, FinChat saw significant but declining web traffic in Q4’23, indicating a transition from curiosity-driven to value-seeking user engagement.
Magnifi is an AI-copilot for individual investors. Magnifi analyzes connected accounts to identify excessive fees, risk exposure, and improvement opportunities. Magnifi’s conversational AI is the only SEC-regulated AI that provides assistance, education, and the ability to buy and sell securities.
FinChat is a public equity research platform for investors, similar to ChatGPT for finance. Finchat provides comprehensive insights from its database of financial information with proper citations through an interactive chat interface. It amassed 100K users within a month of its launch in April 2023.
Some startups with extraordinary QoQ growth in website traffic:
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In the ever-evolving banking landscape, customer demands for simplicity, speed, and personalized services are driving significant changes. Fuelled by their expertise in digital solutions, banking tech challengers are at the forefront, reshaping industry norms. They're pioneering advancements in digital-first services, innovative lending, cybersecurity, blockchain, and AI, ushering in transformative shifts.
This report unveils 30 high-growth startups revolutionizing banking technology, offering key insights into their growth trajectories:
The report further dives deeper into the top performers across different funding rounds based on alternative data metrics such as web traffic and workforce size.
Download the complete report today!
Retailers are making significant investments in personalization, omni-channel experience, analytics, digital store capabilities, AR/VR, robotics, and more, to build new experiences for their consumers. These innovations are not merely disrupting the retail space—they are defining its future!
In this report, we've identified and spotlighted 25 promising early-stage startups that are spearheading innovative advancements in retail technology.
Key Findings:
The report further dives deeper into the top performers across different funding rounds based on alternative data metrics such as web traffic and workforce size.
Download the complete report today!
Get exclusive access to our global events calendar tailored for VCs and PEs, featuring the most anticipated investor and startup events. Find crucial details like dates, locations, and more all in one place.
Rest assured, we've curated the entire event landscape, so you won’t miss any.
.....and more.
Seize the opportunity to enhance your portfolio, expand your network, and stay ahead of the curve.
Which of your portfolio companies have a CAC Payback period of under 1 year? How do the ERP companies in your portfolio accelerate growth after hitting the $10M mark? How do the S&M and R&D costs of your portfolio companies compare to their publicly traded competitors?
We posed these critical questions to over 100+ VC/PE professionals and received a telling response—a moment of silence.
The reality is stark: even leading VC/PE firms are grappling with the challenges of disparate data sources (pdf, excel, decks!!), fragmented insights, and operational inefficiencies. Despite the wealth of data at their fingertips, many are struggling to harness its full potential!
This webinar was aimed to dive deep into the heart of these challenges.
Join us as we explore strategies to transform raw portfolio data into strategic insights, streamline fund operations, and cultivate collaborative success with internal & external stakeholders.
In this interactive session, we discussed:
Open source (OS) is powering Gen AI innovation. Thanks to widely available academic research and platforms like GitHub and Hugging Face, we're witnessing a boom in major projects with impressive outcomes. Despite the considerable resources—money, computing power, and data—that closed-source tech giants pour into AI, open-source initiatives are tracking their growth and performance remarkably well.
A leaked internal memo from Google went viral in 2023 for its observation that open source AI has been subtly yet effectively "eating the lunch" of big tech companies like Google and OpenAI, boasting greater speed, adaptability, privacy, and overall efficiency. Open source AI is rapidly gaining on closed source in both popularity and performance – OS models like Mistral, Llama are catching up with and even outperforming some closed-source models.
As a result, open source AI is seeing substantial interest from developers, researchers and investors alike. Github witnessed a 148% YOY increase in developer contribution to Gen AI projects in 2023. More than $8B has been invested in open source AI over the last 2 years.
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Specifically for Gen AI, the term “open source” typically implies that the source code, any applicable weights and parameters (for training models) of these components are publicly accessible, usable, modifiable, and their distribution is permitted.
Adhering to this definition, the open source AI stack includes comprehensive set of tools to build Gen AI applications - foundational models (such as Llama, Mistral), developer tools & frameworks (such as Langchain, Fixie), model training platforms (such as Weights & Biases, Anyscale), and monitoring tools (Datadog, Seldon).
Open source AI innovation is thriving with new projects and developers
Open source Gen AI projects are seeing significant and growing projects as well as contributors. Last year, Github witnessed 148% YOY growth in contributors and a 248% YOY growth in the total number of Gen AI projects. There are 60K Gen AI projects on Github and over 400K models on Huggingface as of 2023.
Contributor set is becoming increasingly Global, not restricted to US and Europe
Beyond the US and Europe – where a majority of open source projects originate from – the highest number of individual contributors to open source Gen AI came from India and Japan in 2023. Developers from Hong Kong, UK, Brazil, Germany and Singapore are also making numerous contributions to open source Gen AI. By 2027, India is projected to overtake US as the largest developer community on Github.
Steady increase in serious contributors, while “tourist” interest has tempered since Q1 hype
Gen AI overall has experienced a shift from initial widespread hype (peaking in Q1) to more focused and value-driven engagement - the "trough of disillusionment" phase, where initial excitement gives way to sustained, serious development.
Similar trend can be seen in # of stars across Github repos - the growth has tempered since Q1. On the other hand, serious developers (# of contributors to these projects) have grown steadily - 148% cumulatively in 2023.
Python is the preferred language for open source AI
While Javascript has been the top programming language on Github in 2023, Python is the top choice when it comes to AI repositories. Python’s preference for ML projects has carried over to Gen AI because of its comprehensive ML libraries like TensorFlow and PyTorch. Python's flexibility in data handling and its platform-independent nature make it highly adaptable for diverse AI projects.
Mojo, a variation of Python that combines the usability of Python with the performance of C++, is gaining traction as an AI-specific programming language. In Q4’23, Mojo saw a 73% MOM increase in Github stars, indicative of the repo’s popularity amongst developers.
AI repositories favouring more protective licensing
A disproportionate share of AI repos are using the Apache License, under which developers can claim patents on derivative projects. The Apache license is known to be extensive in legal terminology and therefore offer better patent protection than other licenses. Though the open MIT license is the most popular across Github; Gen AI developers are predictably keen on securing their work with more protective licensing.
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Foundational models and developer tools, the core stack of AI, are the focus areas for new startups
Over 60% of new companies in the open source AI space are focusing on foundational models and developer tools, the core elements of the AI stack. This is expected, given that these components are fundamental for building, deploying, and managing generative AI applications across various use cases. Innovation in other areas like model training, fine-tuning tools, monitoring tools, and cloud computing services primarily revolves around these core AI stack elements.
High-quality open source AI reducing reliance on proprietary big tech AI, but data is key
The volume and quality of open source AI is sufficiently robust, enabling developers and startups to effectively compete with proprietary solutions. OS model Mixtral 8x7B surprassed closed source GPT 3.5 in chatbot as well as holistic performance. Other OS models like Llama, Yi are not far behind.
However, a crucial advantage that big tech firms with closed systems hold is their access to extensive data resources. This is evident in the fact that some recent OS models such as Llama-2 or Mistral 7B do not open source their training data. Data is likely to be the key proprietary element in the space.
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Gen AI infrastructure, due to its heavy reliance on vast amounts of data, extensive research, and substantial compute power, requires significant capital investment, has led to larger funding rounds compared to typical enterprise solutions.
Robust funding activity in 2022-23; foundational models and model training software secured maximum dollars
75% of open source AI startups secured funding in 2022-23. Foundational models and model training/fine-tuning software have attracted >70% of the investment dollars.
Nvidia, a leading graphics chips manufacturer for AI, has been a strategic investor in this space, with investments in top startups like Mistral AI and Adept AI.
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Foundational models have varying degrees of openness – for example, Llama-2 has a publicly accessible code, but its training data has not been made public. We have considered models to be truly 'open source' when their core components – the source code, training weights, and parameters – are publicly available and unrestricted for use, modification, and distribution.
Open source LLMs Falcon and Bloom have received significant engagement
Falcon, a large language model (LLM) developed by Abu Dhabi's Technology Innovation Institute, and BLOOM, created by the collaborative research organization BigScience, have recorded the highest downloads on Huggingface – surpassing Meta’s Llama-2.
Launched recently, Mistral AI’s models Mistral 7B and Mixtral 8x7B have gained significant popularity, surpassing many established models on Huggingface in terms of traction.
Open source AI models are not far behind closed source models
Although closed source big models like GPT4 and Claude are at the top of the chatbot leaderboard, open source models like Mistral, Vicuna, Yi, Llama are catching up – which bodes well for the ecosystem.
However, closed source models are still a step ahead according to the MMLU benchmark, which tests knowledge and problem-solving skills across 57 subjects in humanities, social sciences, and STEM. MMLU measures a model's comprehensive performance, and in this context, closed source models like GPT and Gemini continue to outperform open source alternatives.
Open source development is leading to higher efficiency in models
Startups working with open source AI, who don’t posses the extensive data resources or compute power of major tech firms, are motivated to create more efficient models that deliver high-quality results with less computational demand. Mixtral 8x7B, an 85B-parameter 'mixture of experts' model that operates with the compute power of just a 14B model. It has outperformed all other open source models, including the larger Llama-2 70B, in terms of efficiency and effectiveness. This will be crucial in making these models more accessible to local applications (e.g. voice assistants on mobile).
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AutoGPT, Modular’s Mojo are witnessing high developer traction
As a primary platform for developers to interact with and contribute to open source AI projects, GitHub activity tends to be a strong indicator of traction. GitHub stars (similar to a “follow” on social media) are a direct indicator of a project’s popularity on GitHub.
AutoGPT, an autonomous AI assistant built on GPT4, has received significant developer traction. The model is capable of acting as an AI agent, breaking a large task into various sub-tasks without the need for user input, which are then chained together and performed sequentially to yield a larger result. AutoGPT is also capable of connecting to the internet, thereby allowing for up-to-date information retrieval for its tasks.
ModularML's Mojo is a variation of Python tailored for high-performance AI applications, balancing the efficiency of languages like C++ and Rust with Python's simplicity. Mojo's core goals are to streamline AI development, integrate AI/ML infrastructure seamlessly, and deliver robust performance.
Pytorch, Huggingface, AutoGPT, and Supabase stand out with the significant engagement on Github
Github contributors are developers who make changes (known as “commits”) to the code, actively engaging with the repo to improve it. Contributors are indicative of serious developer activity on repos.
The year-over-year GitHub analysis for 2022-23 highlights a notable uptick in both interest and active engagement with various repositories among the developer community.
Though AI agents are still in an experimental phase from a customer-facing POV, GitHub data reveals substantial developer activity in this domain, which could likely lead to some agent-based AI apps emerging soon. AutoGPT, an AI agent repo, is experiencing significant developer activity on GitHub. Other AI agent repos like Bloop, X-Agent are also seeing similar interest from the developer community.
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In 2023, the VC/PE investment landscape witnessed a marked shift towards caution amidst market unpredictability. Yet, even in this deceleration, standout domains such as AI, EV, and Health Tech not only weathered the storm but also secured a notable chunk of the investment landscape. As we pivot into 2024, projections suggest an inclination towards more discerning, data-informed investment approaches, underscoring sectors that epitomize both innovation and expansion.
This analysis delves into 50 dynamic enterprises that showcased significant momentum in 2023, eclipsing their contemporaries by metrics like web traffic, team expansion, and product endorsements, among other indicators!
Key Findings
Don’t miss out on these promising startups! Download the report now.
Climate Tech companies are revolutionizing global markets through innovative and sustainable solutions. These forward-thinking enterprises are gaining increasing attention from investors, not only for their substantial positive impact but also for their remarkable ROI. A prime example is the dominance of Electric Vehicle (EV) companies, which accounted for over half of the top 10 deals in the United States during Q3'23.
Within this comprehensive report, we've hand-picked 30 high-growth early-stage startups that are driving positive change and shaping a more sustainable future for all.
Key Findings:
Healthcare’s LLM moment is well on its way, with Generative AI well placed to reduce labour costs and increase efficiency in this famously labour-intensive industry. In the US, Gen AI could lead to 5 to 10% in savings, translating into $200 billion to $360 billion annually, by automating documentation, research and diagnostic assistance, creating bespoke reports, and facilitating round-the-clock AI healthcare support. In the long term, this impact could go beyond efficiency gains and lead to significant improvement in patient outcomes.
Introducing new tech in healthcare has unique challenges – accessing data at scale, integrating into workflows, navigating regulation, and building trust with clinicians. However, Gen AI has the potential to take on these challenges and disrupt the sector, evident in the investment Healthcare AI is attracting. Healthcare attracted the most amount of AI-related private investment across all sectors in 2022. Major tech companies such as Microsoft, Google, and Amazon Web Services are forging key partnerships with healthcare giants like Epic and Mayo Clinic to drive LLM adoption.
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Healthcare tools address a broad range of use-cases, from pharmaceutical research to patient care and administrative tasks. Healthcare has long relied on Machine Learning to improve medical imaging, treatment prediction, and drug development. LLMs specifically (which are the focus of this article), are driving a new wave of innovation in the space, by optimizing effort-intensive processes that involve unstructured language-oriented tasks.
LLM adoption is the most prominent in provider tools (tools that support clinicians and administrators in delivering patient care)
Early adoption of LLMs is being seen in tools for documentation, clinical decision-making, administrative workflows, and patient engagement
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Emerging tools in the sector face high barriers to entry
The healthcare tools sector is an expansive one, and barriers to entry are high – its rigorous R&D demands and tight regulatory controls pose a tough environment for new entrants. Gaining traction among primary healthcare providers, such as doctors and hospitals, is particularly difficult because of the entrenched preferences for existing technologies and apprehensions regarding the risks and compliance issues associated with new technologies. This landscape poses a significant challenge for new entrants seeking market penetration. Similar to the legaltech sector, the high bar for accuracy in healthcare means mature startups are primarily driving LLM adoption instead of new entrants.
Established healthcare players are well positioned to adopt Gen AI into their existing tools
Recognizing the potential of LLMs, a number of industry incumbents such as Epic, Cedar, Paige AI and Augmedix are enhancing their software with GenAI/LLM capabilities. These established players, having already navigated the sector's regulatory complexities, are well-positioned for AI-driven enhancements. With their established customer networks, they also carry a data and distribution advantage.
New entrants are primarily innovating with Foundational Models and clinician tools
Most clinician tools using LLMs do not directly interact with patients or take any decisions, thereby requiring relatively less regulatory oversight or FDA approvals. Emerging startups are thus innovating with clinician tools which provide insights and support to providers (such as Ambience for documentation, Atropos Health for clinical research, Decoda Health for patient interaction), and healthcare-specific foundational models that power these tools (Hippocratic AI , OpenEvidence).
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The healthcare tools sector typically sees larger funding rounds than other enterprise solutions, driven by its advanced technology needs and intense R&D. The sector's complex use cases, strict regulations, and tough market entry conditions lead to investors placing focused bets on high-conviction companies.
The sector is seeing significant investment in established tools, specifically focused on incorporating generative AI
~70% companies in the sector have raised funds in 2022-23
The funding landscape has been robust in the recent years. Among emerging tools, those spearheading the development of Foundational LLMs for the healthcare sector, like Hippocratic AI and OpenEvidence, have secured some of the largest funding rounds.
Active, yet concentrated funding landscape
Investors are making few high-conviction bets rather than many speculative ones. The sector shows a limited presence of unfunded startups – a deviation from typical trends observed in other AI tools sectors, indicating the formidable entry barriers that define this space.
Hippocratic AI, incubated at General Catalyst, is building a patient-facing healthcare LLM designed for non-diagnostic tasks such as dietary guidance, pre-op conversations, communication of negative test results and billing explanations. Hippocratic AI has developed a framework to incorporate professional certification, reinforcement learning from human feedback (RLHF) via healthcare experts, and bedside manner training into their conversational LLM.
OpenEvidence is an LLM that uses AI to aggregate, synthesize, and visualize clinical evidence for improved decision-making in healthcare. OpenEvidence, in partnership with global healthcare leader Elsevier Health, powers ClinicalKeyAI – an AI tool that supports clinical decision making at the point-of-care by providing quick access to the latest evidence-based medical knowledge through conversational search.
Ambience Healthcare, backed by the Open AI Startup Fund, has raised $36M funding, with a recent Series A round. Ambience's AutoScribe, an AI medical scribe, captures real-time clinician-patient conversations; supporting multiple languages, accents, and speakers. The tool allows clinicians to customize the charting style for better relevance to their function, and provides longitudinal context by integrating historical data from the EMR to capture all relevant info for the visit.
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Employee growth serves as a key barometer of organizational health and confidence in future growth, underlining the sector's momentum in innovation and market outreach.
The sector's employee growth has been consistently positive, despite some fluctuations. After a peak 9% increase in Q2’22, employee growth has stabilized, commensurate with the broader AI market trend where the initial AI hype is giving way to a focus on value. However, overall steady growth trend remains a positive indicator for the sector's health - with the last two quarters seeing nearly 70% of companies in the AI healthcare sector expanding their teams. The growth is relatively evenly distributed across departments like engineering, research, and sales & marketing.
Emerging AI-native startups building healthcare-specific Foundational Models, e.g. Hippocratic AI and OpenEvidence, have seen extraordinary employee growth, with >50% QoQ growth in Q3’23. Employee expansion at these startups has been concentrated mainly in healthcare services, engineering, and business development teams.
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Private investors are increasingly utilizing sophisticated data & tools to identify and evaluate investment opportunities - gaining an edge in both the quality and speed of deals. In this webinar, we have explored the evolving tech stack in Venture Capital - from basic components to cutting-edge tools as well as upcoming experimental applications for the future.
In this on-demand webinar, you’ll learn about:
The global big data market is expected to reach $273 billion by 2026, with several high-value companies like Databricks topping the charts.
In this report, we focused on early-stage big data companies and picked 23 high-growth big data startups that foster innovations in areas like data analytics, data storage, data transformation, and more.
Key Findings:
The report further dives deeper into the top performers across different funding rounds based on alternative data metrics such as web traffic and workforce size. For companies with open-source projects, we have further analyzed Github metrics using cumulative stars.
Download the complete report today!
Deep Tech innovations are based on cutting-edge scientific discoveries and complex engineering solutions. They have a strong IP moat and are usually formed with the intent to disrupt existing industries and create entirely new markets!
In this report, we have identified 30 high-growth startups in 2023, leveraging Deep Tech to foster innovations.
Key Findings:
Download the complete report to explore promising Deep Tech startups across diverse emerging categories.
The global Digital Health market is expected to reach $1.5 trillion by 2030, growing at a CAGR of 27.7% from 2022 to 2030.
The Digital Health 30 is created as a one-stop resource for investors looking to gain exposure to this exciting and growing market. Our index provides investors with a curated set of 30 high-growth companies across different stages of funding and growth in their lifecycle.
Key Findings:
This report provides deep insights into each company’s performance based on alternative data metrics like website traffic growth, employee growth, product ratings, and many others available on the Synaptic platform.
Download the complete report now!
The fast-growing Fintech space is projected to reach $492 billion by 2028. With over 26,000 Fintech startups worldwide, investors have tremendous potential to diversify their portfolios across lending, payments, personal finance, and investments among other verticals.
In this report, we have identified 25 high-growth Fintech startups focused on consumers. Our report dives deeper into the top performers through alternative data metrics like web traffic and workforce size. Not only that, we also feature leading founders and investors in this space!
Key Findings:
Download the complete report to explore promising consumer Fintech startups across diverse emerging categories.
What does the quintessential Generative AI founder look like?
Entrepreneurs building generative AI have captivated investor interest. Since the advent of LLMs, generative AI startups have attracted over $26B in investments, 70% of which occurred in 2023 itself.
We have gleaned insights by analyzing a comprehensive dataset of 5800+ founders across 3200+ Gen AI startups. This report aims to delineate the typical generative AI founder by analyzing their academic backgrounds and previous work experience.
Key findings include:
Download the report to learn more about their key traits, understand how these traits correlate with funding outcomes, and how the profiles of generative AI founders differ from those in a random sample of startups or unicorns.
The global e-commerce market is surging with a projected 27.43% CAGR for 2023-2028, driven by digital adoption, AI-powered personalization, the rise of D2C brands, and evolving consumer preferences.
Our "Consumer Ecommerce Champions" list compiles 25 standout companies across categories like apparel, beauty, appliances, mobility, health and more at different stages of growth. These lesser-known companies are filtered based on 100+ alternative data metrics for interested investors.
Key Findings
The report also highlights the leading investors and the visionary founders behind the top companies in each funding stage.
Dive into the details and explore investment opportunities in the consumer e-commerce sector with our comprehensive analysis.
The open-source landscape has witnessed a seismic shift in investor interest, driven by the resounding successes of projects like ChatGPT and Grafana. With over 100 million developers and 28 million public repositories, GitHub hosts a treasure trove of high-growth, transformative projects!
In this report, we offer you a roadmap to navigate the open-source goldmine. We've examined the top 20 open-source projects, selecting them based on key GitHub metrics that illuminate popularity, adoption, and commercial viability.
Key Findings
The report also highlights the leading investors and the visionary founders behind the top open-source startups at each funding stage.
Join us on this journey as we uncover the Open Source Gems, guiding you towards promising tech ventures with the potential for remarkable returns.
Tiago Forte’s idea of "Second brain" – using digital tools to create an external system to efficiently organize and access your knowledge – has been a cult classic amongst productivity buffs. Generative AI is giving a literal meaning to Second brain. By adding contextual understanding and ability to take decisions, Gen AI can function as a second brain in your pocket - organizing and accessing your knowledge and executing tasks on your behalf.
While all AI tools enhance productivity at their core, the term “productivity tools” is reserved for business function agnostic tools that help optimize day-to-day tasks, workflows, knowledge management and communication. Application of Gen AI to these tools is generating substantial excitement and the value is starting to show. A recent Mckinsey study identified collaboration, planning and knowledge management as areas where generative AI could have the biggest impact. David Ulevitch, General Partner at a16z compared the note-taking app Mem.ai to J.A.R.V.I.S from Iron Man – an intelligent personal assistant that surfaces the information needed, where and when it’s needed.
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Microsoft vs Google is the defining battle in AI-enabled workspace, while Apple’s entry is anticipated
Microsoft has been a pioneer for generative AI in this space, with the launch of Microsoft Copilot – an AI-powered assistant integrated into Windows 11, Microsoft 365, and Edge and Bing web browsers to offer personalized, context-aware assistance based on web intelligence, work data, and user's PC activities.
Google is following suit by embedding generative AI into its Workspace via Duet AI – which will offer features like drafting responses in Gmail with minimal prompts, generating original images from text in Google Slides, automating data classification in Google Sheets, etc. – simplifying tasks and enhancing efficiency across professional and personal contexts.
The core AI features that Microsoft and Google offer in their productivity tools will determine the battlefield where startups innovate in the long run - downstream use-cases from those core features. While Apple remains silent on introducing generative AI to iWork, there are strong indications of intensive R&D underway, suggesting a potential entry in the near future. It’ll be interesting to see which tech giant can more seamlessly integrate gen AI and deliver value through their respective platforms.
Broadly two types of interfaces are emerging – assistants and agents
AI Assistants are more established, since they enable a layer of human oversight. While the idea of AI agents is exciting; they are still largely unproven and expensive - assigning tasks to agents without human oversight is not entirely reliable until the LLMs reach a certain threshold of accuracy.
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A highly competitive landscape with 100+ tools; the typical growth path for startups is to iterate in the prosumer market and eventually graduate to enterprise
Similar to the AI video tools (~50 AI-native startups) and AI coding tools (100+ startups) sectors, the AI productivity tools sector has a large lower-end market with a diverse set of individual / SMB use cases - an ideal building ground for startups to build market presence, iterate on product and eventually break into enterprises. Productivity tools, in particular, has been a space where the individual usage can drive enterprise sales. Unicorns like Notion, Airtable and many others started out serving individuals, building a strong product which those individuals eventually pitched to their employers
New startups are building for niches across verticals, use-cases and modalities
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A robust funding landscape over the last 2 years, with several deals across stages from Pre-seed to Series F
Over 70% of the funded startups in the space received their funding in the last 2 years. Investors are placing active bets on new, AI-native startups.
Enterprise tools are attracting the maximum share of funding dollars
~75% of the funding for new startups has gone to the ones focussed on enterprise use-cases. This is to be expected – as discussed earlier, the typical growth trajectory for startups is to graduate to enterprise users after serving prosumers. Once integrated into enterprise systems; these tools become indispensable to organizational workflows, making them sticky and thus attractive for investment.
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The sector is experiencing a tempering of interest after the initial Q1 hype, indicating a possible trough of disillusionment
The initial hype around AI has been high in general in Q1’2023, but a lot of sectors have observed a tempering of interest over Q2. In the AI productivity tools sector, ~72% of startups witnessed positive QoQ growth in website visits in Q1’2023. However, 75% of these startups showed a decline in Q2’2023. Similar to the AI Customer Support Tools market, this sector shows signs of experiencing a ‘trough of disillusionment’ – a trend common in emerging tech markets, where the initial interest surges, but settles down into a more realistic level as the realities of implementation and market adoption come into focus. ChatGPT experienced a similar pattern, noting around a 10% decline in global traffic for two successive months in Q2’23.
Consumer interest in AI productivity tools is likely to follow the same trend, transitioning from trend-driven curiosity to a more value-oriented engagement.
Taskade, an AI-powered productivity app, has garnered strong consumer interest in Q2’23. Taskade harnesses AI to generate task lists, mind maps and custom workflows. It also allows users to chat with AI within any project to generate creative ideas or automate recurring tasks.
Fireflies.ai, an automated AI note-taking and meeting assistant tool, has shown remarkable growth this year with >200% QoQ growth in Q1’23. The platform uses AI to transcribe meetings in real-time and sync meeting data with platforms such as Asana / Slack / Hubspot, connecting systems of communication with systems of record. Fireflies’ add-on AskFred is ChatGPT for meetings, wherein the user can ask AI to screen notes and fetch any information from the meeting, score calls, write corresponding emails or blogs, etc.
Capacities.io has shown remarkable growth in website traffic since its launch in 2022, with 270% QoQ growth in Q2’2023. Capacities.io serves as a mental studio, allowing users to store, connect, and reflect on information, fostering creativity and understanding. Capacities currently offers an AI chatbot for summaries and translation, built-in AI commands for optimising the information (such as listing action items, takeaways, elongate or shorten text, etc), and context-aware AI autofill for properties.
Reflect.app is another such note-taking tool which has grown its website visits by 115% QoQ in Q2’2023. Reflect is designed to align with cognitive processes, combining speed, aesthetics, and security. Reflect AI allows users to transcribe voice notes, generate frameworks from thoughts, list action items or build custom prompts for AI. It offers features such as networked and backlinked notes, calendar integration, and Kindle note support across web and iOS platforms.
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Process Street is a versatile process management platform which boasts of enterprises clients like Salesforce, Spotify, Airbnb, Drift and 3000+ other businesses. Process Street's AI WorkFlow Generator creates tailored workflows with built-in timelines, task allocation, and approval processes. The platform also provides AI-generated task instructions, drafts emails, and allows customization and integration with other tools such as Slack, Trello, and Zapier, helping to tackle common business challenges and enhance operational efficiency.
Superhuman is a popular email client for productivity, known for its extremely loyal customer base. Superhuman aims to be the “fastest email experience” created, and the tool is known to propagate Merlin Mann’s idea of “Inbox Zero” by triaging the inbox quickly and effectively. Superhuman AI, powered by ChatGPT, is capable of generating emails which match the tone and voice of the user, auto-correct realtime, polish writing, and research pretty much anything which might help you write the email, within the platform itself. Some of Superhuman’s clients include organizations such as Spotify, Netflix, Uber, Notion, Dropbox, and Harvard.
Spike is a conversational email tool that turns inboxes into a chat-like experience, where emails are grouped by people and not threads/topics. Spike is preferred by many due to its unified inbox, which consolidates Gmail, YahooMail, Outlook, etc. Spike's Magic AI is capable of creating context-aware responses and summarizing lengthy emails. The Magic AI Bot acts as a AI chatbot within the inbox, which the user can feed prompts to for various requests, including question answering and code generation. Spike is used by teams at Spotify, Indeed, and Fiverr, amongst others.
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Generative AI is revolutionizing customer support by resolving the dilemma of scaling while personalizing interactions. A recent study by NBER showed that AI implementation can reduce the average handling time of support tickets significantly while maintaining NPS scores.
Our report provides a curated set of high-growth companies across different stages of funding and growth in their lifecycle. Key insights of the report:
This report also deep dives into each company’s performance based on alternative data metrics like website traffic growth, employee growth, product ratings, and others available on the Synaptic platform.
Download the complete report now!
Finding the right generative AI founders is on top of mind for many private investors today. Direct searches on people's databases often cast a very broad net, especially with "Artificial Intelligence" being overused as a tag today. Investors usually find themselves sifting through a multitude of profiles to identify those founders that truly align with their focus. To address this, we've curated a pinpointed list of interesting founders who are innovating in the Generative AI space. We have tracked prominent projects across sources like TheresanAIforthat, Github, Product Hunt, and Hugging Face, and enriched them with Synaptic People Data.
What's Inside?
Download the database and expedite your search for Generative AI disruptors!
Enterprise Software 50 is a curated list of 50 high-growth firms at varying stages of development, each strategically positioned to dominate sectors within Enterprise software including HR Tech, Marketing Tech, Sales Tech, Data Analytics, Enterprise Productivity, Operations Tech, and Security, Risk, and Compliance.
This comprehensive report offers nuanced insights into the industry's standout performers. Leveraging a plethora of alternative datasets—ranging from website traffic and employee growth to product ratings—we present a multi-dimensional view of leading companies.
Key Insights:
The report also covers the top investors in the enterprise software space. Additionally, the report provides detailed company performance metrics for the high flyer enterprise software companies and highlights the leading founders in this space.
Download the complete report now!
Generative AI is emerging as a groundbreaking solution to a conundrum at the heart of customer support: the need to simultaneously scale and personalize interactions. Traditional customer service, predominantly a human-centric endeavor, demands extensive training, faces cultural and linguistic barriers, and often grapples with scalability. First-generation automations, such as chatbots and IVRs, were primarily rule-based, offering scalability but lacking the finesse of personalization. Generative AI can bridge this gap, and the impact is measurable already.
A recent study by NBER showed that AI implementation can reduce Average Handling Time (AHT) of support tickets significantly, while maintaining NPS scores. JetBlue's implementation of ASAPP’s generative AI solution saved their contact center an average of 280 seconds per chat, resulting in 73,000 hours of agent time saved in just one quarter.
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Customer support is an extremely competitive market with 50+ established players specializing across customer segments, communication channels and interaction types
Incumbents and unicorns driving AI innovation in this sector
Tech giants have already hopped on the generative AI bandwagon – Salesforce's EinsteinGPT, Freshworks' Freddy, Intercom's Fin, Zendesk AI and Zoho's Zia are notable examples.
Compared to markets like AI Copywriting and AI Video Tools where new AI-native startups led innovation and were able to scale substantially (e.g. Jasper, Synthesia etc.), the AI innovation in customer support is being driven by incumbents and established startups. The set of new players is nascent in terms of adoption and funding. This trend is driven by -
Incumbents focussing on end-to-end product suites, startups building for specific point solutions
Most incumbents in this sector are offering comprehensive, 360-degree solutions that address a wide range of support needs – multichannel conversational support, ticketing and workflow automation, agent support and productivity tools, and customer experience insights. Startups (both new and established) are typically focussed on point solutions such as AI chatbots, response drafting, query summarization etc. and focus on specific channels / modalities.
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Conversational AI and Agent Assist are key use-cases attracting innovation
Both Conversational AI and Agent Assist tools are serving use-cases which involve direct, customer-facing tasks; hence, generative AI solutions can potentially diminish the dependency on manual effort, improve TAT and enable efficient scaling of customer support operations. Conversational AI, in particular, is relevant across businesses of all sizes and industries, given the need of quality customer support in a digital-first world. Agent support tools are typically more geared towards contact centers and large enterprises.
A bustling market with several startups carving out niches in different use cases, industry-specific solutions
Given the ubiquity of AI-based conversational and agent support tools / features, new startups in the space are trying to differentiate in unique use cases like agent performance mgmt., feedback analysis, intelligent build tools etc. Some startups have also focused on industry-specific customer support tools like MeetElise for real estate, Prodigal and Kasisto for banking, and Yuma for e-commerce, which can be trained on customer support data specific to the respective industry.
Building from low-cost geographies, for the World - is a common theme among startups
Customer service is a price-sensitive function for companies, since operational costs grow with the scale of the organization. Startups located in low-cost geographies like India are well positioned to offer tools at lower price points, while offering competitive features. Several funded startups are originating from India, such as Gupshup, Haptik, Verloop, Kommunicate, Rezo, and Alltius – leveraging price-competitiveness as well as acquired knowledge from historical outsourcing of customer service to India.
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Recent funding dollars have mostly gone towards Incumbents & Established players
~95% of the funding dollars in 2022-23 has been directed towards incumbents and mature startups. Established players are safe bets in this space as explained in the Player Archetypes section - with large, sticky customer bases and substantial data, they are very well positioned to drive value from AI.
There are a large number of well-funded players in the market, making this a very competitive space
There are 10 startups who have raised over $50 million in funding – and this is excluding the 12 unicorns and big tech players, 5 of which also received recent funding, like Dialpad and Cresta.
New, AI-native startups have attracted smaller, speculative bets
Investors haven’t shied away from demonstrating interest in new, AI-native startups; albeit with smaller, speculative bets. Although new startups represent only ~3% of total funding for 2022-23, they comprise >40% of the funding deals made during that period.
Market going through a “trough of disillusionment” as initial AI hype settles down
AI customer support tools, like other AI markets, saw strong interest during the peak of the AI hype in Q4’22 and Q1’23. However, about ~70% of startups that experienced growth in Q4’22 and Q1’23 have reported de-growth in Q2’23. This trend is common in emerging tech markets, where the initial interest surges, but settles down into a more realistic level as the realities of implementation and market adoption come into focus. [ChatGPT experienced a similar pattern, noting around a 10% decline in global traffic for two successive months in Q2’23.] Going forward, we expect steadily growing interest in tools that can prove successful value creation for customers, while less differentiated tools will struggle - in other words, we expect customer attention to be driven by results vs by hype going forward.
Sapling, Verloop and MonkeyLearn showing strong traction
Sapling.ai recorded impressive consumer interest at >4M visits in Q2’23. Sapling’s AI-copilot comes as a web extension and also integrates with CRMs or traditional communication platforms such as Gmail or Slack to optimize agent communication by retrieving relevant responses, rephrasing and enhancing language. The platform offers a comprehensive snippet library and actionable analytics from customer conversations.
Verloop is an India-focused agent-assist platform which offers 24/7, multilingual AI-powered support across chats, calls, and soon, emails. The platform has been extremely focussed on certain verticals, including ecommerce, banking and FSI. Verloop has worked with brands like WhatsApp Business, Nykaa (which saw a 2.2x rise in engagement post-automation with Verloop), and Ninjacart (addressing 689% more queries post-automation).
MonkeyLearn – used by computer tech giant Dell to save 400+ employee hours on feedback analysis – offers a no-code AI toolkit for customer support teams. It simplifies AI adoption, enabling teams to automate support tickets, analyze customer feedback, gauge sentiment and NPS, and visualize the results graphically.
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Boost.ai has earned acclaim for its advanced conversational AI capabilities – it was recognized in Gartner® Magic Quadrant™ for Enterprise Conversational AI Platforms for two consecutive years (2022 & 2023). Boost.ai deploys advanced virtual agents tailored to manage thousands of industry-specific intents with a 90% resolution rate. Their conversational AI adapts swiftly to customer feedback, supported by a user-friendly no-code builder and AI trainer, empowering enterprises to scale and refine customer service effortlessly.
Kommunicate – the chatbot which resolved 75% of GAP’s customer interactions in Chile – stands out for its blend of bot and human-driven interactions. Users laud its dashboard for clarity and the software's knack for streamlining communication.
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Artificial intelligence (AI) is the future of business. The global AI market is projected to grow at a CAGR of 37.3% from 2023 to 2030, reaching $1.8 trillion by 2030. This remarkable expansion is being driven by the increased impact of AI on business outcomes and productivity.
This report provides investors with a list of 50 high-growth private AI companies well-positioned to capitalize on the future of AI across its foundational and diverse application software.
Key Insights:
This report provides deep insights into the high flyers AI companies and includes performance metrics based on alternative datasets like website traffic, employee growth, product ratings, and many others available on the Synaptic platform.
Download the complete report now!
Generative AI will transform video content creation. Video content has exploded in the past decade, with the average consumer now watching ~17 hours of video in a week. The diversity of video content is as vast as its volume - from <30s short videos on TikTok to long 3hr+ movies (h/t Nolan). Despite its ubiquity, video content creation remains a resource and time-intensive endeavor, requiring investment in equipment & software, numerous takes, complex editing & sound engineering skills. A large, multi-segment and effort-intensive market is the perfect breeding ground for AI-led disruption, which is already well underway. Runway’s powerful AI video editing tools are used by top-tier productions such as The Late Show and the Oscar-winning movie, Everything Everywhere All at Once. Synthesia’s video platform serves 50k+ businesses in creating corporate training, marketing, and customer service videos - reportedly reducing production time by a remarkable 95%.
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Incumbents: Apple & Adobe have leading video-editing tools, but late to Gen AI
Pre-2017, the video creators’ toolkit largely included Adobe (Premier Pro & After Effects) or Apple (iMovie & Final Cut Pro). Both have been largely focused on high-end video production and require significant time and training to execute. Meanwhile, tools for casual creators and simpler video content were fairly limited, with most creators using rudimentary editing features offered by YouTube, Instagram and other platforms (basic cutting, filters, framing etc.).
Despite being industry heavyweights, both Apple and Adobe have been relatively late in developing AI for video. It wasn't until April 2023 that Adobe officially unveiled its entry into the generative AI sphere with 'Firefly for Video,' which is still in its development stage and Apple is only rumored to be working on its own AI offerings.
Scale startups: Early AI pioneers have become unicorns by cracking Enterprise use cases and building strong tech differentiation
Runway and Synthesia were pioneers who began experimenting with generative AI in 2017-18 and have emerged as market leading players. Both companies were born out of cutting edge machine learning research, founded by researchers and have built their own proprietary foundation models - but are targeting different use cases.
Runway has focused on developing video editing and visual effects (Vfx) tools targeted at professional editors, filmmakers and broadcasting. Runway has built a deep tech moat with its best-in-class AI video models and launched the first open-ended text-to-video tool (Runway Gen 2) in March ‘23.
Synthesia has carved its niche by deploying AI avatars at an enterprise level - creating video with narration from human-like avatars used for learning & development, customer service and marketing needs among businesses. Today, it counts ~35% of Fortune 100 companies as users.
Surge of new tools: Emergence of new tools leveraging a large creator market and innovating around niche use cases, modalities
The video tools space is a really attractive playing ground for new startups to build and innovate given the presence of a large lower-end market. Casual creators and small businesses building video content for social media presents a large market with low switching costs - ideal for new startups to gain traction, build market voice, get feedback to improve product and eventually either target higher value segments of the market (or build a strong brand that can sustain in the creator market). This is in contrast to markets like Legal Tech, where the lack of a lower-end market has stunted new startups from making a mark and established players have been the primary beneficiaries of AI. The diversity of the video market also presents opportunities for startups to build specialized solutions designed for different use cases and needs.
As a result, there is a plethora of new players in the space specializing across different dimensions -
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Robust fundraising environment
Fundraising in AI Video Tools has been more robust than other AI application markets we’ve looked at, like AI Coding, Legal and even Copywriting tools. While in other markets we’ve seen funding dollars go primarily towards incumbents adding AI features and pioneering startups that have quickly emerged as scale players, there has been a stronger willingness to bet on new startups in the video space (evident in the high number of funded startups in the space, most of which have received funding in the last 12 months). This is likely driven by a combination of -
Top VCs as well as Strategic investors active in the market
Sequoia has been especially active in the space with 5 bets (incl. India and China arms, pre separation) while Andreessen Horowitz, Accel, Kleiner Perkins and Lightspeed have also made multiple bets in the space. Nvidia has backed both the unicorns in the space, Runway and Synthesia, as a strategic investor. The potential value unlock is significant as video is a compute-heavy AI application and Nvidia supplies most of the hardware used to make it possible. OpenAI’s startup fund has also invested in the space with Descript.
Business-focused tools attracting the lion’s share of funding
Unsurprisingly, business tools have attracted a large share of funding in the space. Enterprise tools are preferred by investors given their higher stickiness and customer lifetime value. All of the top 5 funded tools are enterprise focused and ~75% of all funded startups are targeting business use cases.
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Website visits are a strong indicator of traction for AI Video Tools given most tools have web apps for users.
The website traffic and growth for AI Video Tools is astronomical, with 10 startups seeing >5M visits in Q2 and significant number of startups (10+) in the space seeing >50% growth QoQ. For contrast, among AI Coding Tools only 3 startups averaged >5M visits in the same period with 1 among the top 10 growing >50% QoQ. Even in the more popular AI Copywriting tools space, only 5 startups averaged >5M visits and no startup saw >50% growth QoQ.
Fliki.ai, launched in 2021, has seen above 300% QoQ growth in their website visits this year. Fliki’s growth can be attributed to its versatility – it offers an advanced tool that transfigures text, audio, or blogs to video with features of voice cloning and voiceovers, an array of voice and language options and sophisticated editing options for pronunciation, speech rate, dialect and pitch.
D-ID differentiates by enabling users to animate still images into talking avatars that sync with any script. Chat.DID, an interactive web app that allows people to video chat with artificial intelligence, made D-ID the first platform to offer a combination of digital avatars and GPT-powered communication. Additionally, broad range of integrations and API-access further make it a standout product.
Veed surpassed 1 million users in 2022 and is particularly preferred as a all-in-one online video editing platform among social media creators and YouTubers. Veed differentiates by providing a remarkably broad feature set that spans image-to-video, music-to-video, transcription / subtitles, avatars to name a few and massive library of templates for different use cases (casual videos, explainer videos, product videos, training videos) as well as platforms (Facebook, Instagram, TikTok, Twitter, YouTube).
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Pictory has built a loyal customer base of creators, especially podcasters and YouTubers who have lauded the tool for its audio-sync feature (matching audio track to lip movements in the video), the beginner-friendly UI/UX and a well-stocked library from their partnership with Shutterstock.
Descript has received appreciation for its fundamentally different approach of applying a universally familiar document-editing UX to video editing, making it easier for people with low video editing experience to achieve great results.
InVideo has become a preferred tool among creators due to its drag-and-drop interface and 24x7 customer support. The platform's mobile application facilitates video editing on the move. Users especially highlight ‘unlimited’ video templates which span across categories of YouTube and business videos to specialized formats like holidays, sports, education, etc.
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Deal sourcing is a critical part of the deal-making process, it has the potential to drive exponential outcomes by unearthing promising opportunities. However, for most investment firms it is still a highly subjective, manual, and time-consuming process- driven by networking and word of mouth.
Research by Data-driven VC shows that only 1% of venture capital firms are data-driven in their investment decisions.
Source: DDVC; Total number of active VC funds EOP 2022 according to Crunchbase, Dealroom & Pitchbook[1]
Drawing on our experience working with top investors and interviewing data-driven experts, we've developed a strategic and proven deal-sourcing framework to empower investors. In this article, we will deep-dive into the five critical concerns that investors should evaluate for effective deal sourcing.
(Geography, Deal Size, Investors, Stage, Industry, Business Model)
Investors often exhibit specific interests tied to certain criteria while sourcing their deals. Having clarity on geography, industry, and business models can help in staying focussed and maximizing the deal sourcing outcomes.
Investors often exhibit specific interests tied to certain criteria while sourcing their deals. Having clarity on geography, industry, and business models can help in staying focused and maximizing outcomes. For example, an investor firm focusing on emerging markets may target companies operating in Southeast Asia, driven by the region's tremendous growth potential. Similarly, investors interested in clean energy and sustainability might be particularly interested in companies in Northern Europe, given the region's strong commitment to renewable energy initiatives.
Further, an investor might want to narrow opportunities based on the industry and business models. Some investors might look at Fintech and Healthtech verticals while some want to focus on specific business models like Payments within the Fintech vertical and so on. Similarly, few investors might want to back up prominent investors in their funding decisions.
Another consideration is the current funding stage of the business.
For instance, VCs entering deals at Series A would want to source seed-funded companies. On the other hand, PEs might wish to screen companies beyond series D with proven business models and outcomes. Below is an example of quick filtering on energy companies in Europe:
Figure 1: Sourcing Energy companies in Europe with YoY growth % in employee size > 50% using the Synaptic platform
(Leadership Experience, Education Background, Track Record, Employee Feedback)
The expertise, experience, and leadership qualities of the founders and management team can significantly impact a company's success and the ROI.
Here’s how you can further finetune the deal-sourcing process by assessing the management team at a company:
Culture is equally important to understanding a company’s long-term sustainability. Here’s how you can assess the company culture:
Figure 2.1 : Glassdoor summary for Kayak.com on the Synaptic platform
Investors can also improve deal sourcing by leveraging Glassdoor ratings to benchmark against similar/competitive companies for attributing reasoning to industry vs company-level changes.
Figure 2.2: Comparing Glassdoor ratings across factors for Kayak and its competitor, Skyscanner on the Synaptic platform
Analyzing product reviews and ratings provides valuable insights into customer satisfaction and signals potential success in the future. Customer traction helps gauge the company’s competitive advantage, demand, and product adoption rate, which are all helpful in identifying companies with a strong growth trajectory. The Product NPS is one good metric to evaluate product market fit. All these factors together help filter promising companies through the deal-sourcing process.
For tech companies, product ratings and reviews are available on platforms such as G2 and Capterra. For D2C companies, platforms like Amazon offer a glimpse into customer sentiment and product popularity.
For service-based companies, company ratings are also available on Capterra and Google. Qualitative feedback and comments available on these platforms can further provide insights into product strengths and areas of improvement, customer servicing, pricing, and value addition.
Figure 3.1: Overall product ratings and NPS comparison for Lyft Business and Uber Freight on the Synaptic platform (June 2022-June 2023)
Figure 3.2: Subjective reviews for Uber Freight on the Synaptic platform
To assess the customer and PR traction, social listening tools such as Meltwater or Google search trends can be leveraged.
Figure 3.3: Google search trends for two popular goods and transport companies, Uber and Lyft on the Synaptic platform
Mobile app popularity and adoption can also be checked on app stores by looking at the app ratings.
Figure 3.4: Active Users and Download Trends for Uber and Lyft on the Synaptic platform (May ‘21-May ‘23)
By leveraging all the above sources of alternative data, one can gain a comprehensive understanding of a company's product-market fit, customer satisfaction, and overall market potential; all of which are significant filters in the deal-sourcing process.
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(Hiring Trends, Sales and Marketing Spends, Raising Subsequent Funding, Usage Trends with Active Users, App Downloads, Total Website Visits)
Figure 4.1: New job count trend for SirionLabs on the Synaptic platform (Sep 2022-June 2023)
One can also analyze changing employee counts in companies using LinkedIn to gauge growth signals based on company size. Department-wise growth can offer additional insights into the company’s focus on product development, marketing, or sales. For instance, looking at American Giant, a menswear brand that focuses on manufacturing and retailing apparel, we see that despite the overall reduction in employee count, sales and marketing jobs increased in the company- signaling an increased focus on revenue and business growth.
Figure 4.2: Employee distribution for all functions at American Giant on the Synaptic platform (April 2021-April 2023)
For instance, let’s consider a leading private D2C company, Harry's (sold to Big Razor for $1.37 billion in 2019). The Observed Sales have been consistently reducing over the past 2 years signaling reduced online sales or a total drop in revenues, raising red flags and calling for a further deep dive.
Figure 4.3: Observed sales trends for Harry’s on the Synaptic platform (July 2021-May 2023)
Some of the key transaction data metrics that can help in effective investment research include the count of observed customers, average transaction value, sales per customer, transactions per customer, cohort customer retention, and new customers attained by a company.
(Financial Ratios, Cap Tables, Valuations, Exit Probability)
Assessing the financial performance of a company is the most crucial part of the deal-sourcing process. Access to financials is very difficult while sourcing private companies. However, for public markets, this step is a must! One needs to carefully scrutinize factors such as revenue growth, profit margins, cash flow, and return on investment to gauge the company's financial health and potential for future returns. Furthermore, the current valuation of the company plays a vital role in determining the investors' exit strategy.
A favorable valuation allows investors to maximize their returns when they eventually exit the investment through IPOs, acquisitions, or secondary market transactions. Below is a snapshot of the exhaustive financials preview on the Synaptic platform.
Figure 5.1 : Financials snapshot for Uber on the Synaptic platform
2 points of context on Legal Tech are important to know, before delving into AI Legal Tools -
Contract management has been a growth area for Legal Tech over the last 5 years driven by a large TAM (as every company deals with contracts, and contract automation adds significant value) and the fact that you can sell products to corporates, thus avoiding a run-in with the large incumbents at law practices. Contract management tools offer contract drafting / editing, execution and contract analysis features. The application of LLMs has largely been on contact drafting - stepping up template-based drafts to auto-generated drafts from a prompt. Ironclad, LinkSquares and Evisort are series C+ startups with established customer bases that have invested in LLM-based functionality.
Legal documentation tools serve primarily 2 purposes - a) finding information across a large set of documents, also called e-discovery (for e.g. finding relevant facts in a data dump submitted by a counterparty during litigation) and b) drafting documents like patent applications, legal communication etc. (some tools also provide contract drafting features in conjunction). LLM application has largely been on drafting as of now.
Legal assistants are an LLM-native concept that has emerged primarily over the last year. The concept is similar to ChatGPT and GitHub Copilot in creating a chat-based interface for a broad range of assistance. The number of tools exploring this use case are low currently, primarily because of the high accuracy requirement in legal advice. Harvey however has taken a novel approach to crack this market (more on it to follow). DoNotPay has created a new market for automated B2C legal advice, focusing on small disputes that lawyers wouldn't pick and customers would typically drop (e.g. rebates, parking tickets etc.). Tools like E-legal AI and AI Lawyer are also exploring legal chatbots for individual customers for simplifying legal jargon, asking basic legal questions etc., however the use is very nascent.
Legal research has been dominated by Westlaw (Thomson Reuters) and Lexis Nexis - every law practice more or less has access to these tools. Research thus hasn’t been a focus area for startups. Casetext is an exception - it was an early adopter of GPT-3 in 2020 to provide powerful semantic search and has been able to scale off of that.
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In markets like Copywriting and Coding tools, a large set of AI-native startups emerged to pioneer LLM-based functionality. From this set emerged scaled startups that have attracted customer as well as investor interest (e.g. Jasper in Copywriting, Warp in Coding). In the AI Legal tools space, new-age startups haven’t emerged in earnest yet (Harvey is the exception), the drive to LLM-based functionality has come from established startups in the market. Some key factors driving this are -
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Incumbents / established startups are better positioned to deploy LLM use cases than New startups given access to distribution and data. It is therefore not surprising that funding dollars have been directed towards relatively established players in the space.
Ironclad, LinkSquares, Evisort, Casetext, SpotDraft have all raised recent rounds with AI-based features a stated objective in the raise.
Harvey is an exception among new-age startups. An OpenAI funded startup, it hit the headlines for it’s deal with Allen & Overy and has subsequently raised a $21M series A (Apr’23) led by Sequoia. Harvey’s approach has been unique - building bespoke AI models for their customers as collaborative projects vs taking a product-first approach. This has helped them get access and puts them in a great position to attain higher accuracy / relevancy in their offering, which in the future could lead to a killer product which can be transitioned to a product-first model. Perhaps their long waitlist can be an opportunity for other startups looking to capitalize.
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While website visits are not a straightforward indicator of performance in this space as many tools are not just web-based but have integrated applications (like MS Word plugins for contract drafting), there are some clear insights in this data. Casetext and DoNotPay have a substantial and growing user base (10M+ visits is a strong indicator of a big user base) and Spellbook is witnessing superb growth (2.5x visits growth QoQ).
Casetext was an early adopter of GPT3 and was able to get traction in space that has stalwarts like WestLaw (Thomson Reuters) and Lexis Nexis. Recently it has launched Co-counsel a legal assistant. During the publishing of this article, news of Thomson Reuters acquiring Casetext has broken. This makes sense - helps Thomson Reuters keep control of the Legal research space and provides them with a strong LLM-based legal assistant, which they can scale off of their large customer base.
DoNotPay is a pioneer in B2C legal tools, a true market creator. It provides automated legal assistance for small disputes (e.g. rebates, lease disputes, parking ticket appeals etc.) that were often dropped by consumers - resulting in direct savings with very little hassle. LLM-based automated bots have taken the product to another level.
Spellbook is contract management player targeting lawyers and legal practices (which is rare as most contract mgmt. solutions target corporate) with LLM-based automation for complex / bespoke contracts (the kind that would come to lawyers rather than being handled by corporate legal). The native MS word integration and innovative features like negotiation suggestions and auto-diligence set the tool apart. It’s strategic partnership with Thomson Reuters is another differentiator that allows access to customers and data.
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Casetext’s product feedback underscores its strong traction and gives credence to Thomson Reuters’ rationale for acquiring it. Evisort is a well established player and product leader in the contract management space.
Gavel (formerly Documate) is an emerging player in the legal documentation space with a unique approach - Gavel enables legal practices to create client facing workflows that help them deploy legal products along with their services (e.g. Gavel helped a small legal practice launch a rental lease agreement tool for its customers). The strong automation features, workflow integrations, and prompt customer support are key drivers of NPS.
SpotDraft is an emerging player in contract management. The product differentiation is based primarily on superior workflows (large set of integrations, central contract mgmt feature that tracks all changes, tasks related to contracts) and ease of setup.
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AI Legal Tools is a market on the precipice, with clear potential for value but big challenges to achieve practical accuracy; we wait to see who can “raise the bar” and unlock this opportunity.
6d:T664,The global digital health market is expected to reach $1.3 trillion by 2030, growing at a CAGR of 23.7% from 2022 to 2030. The Digital Health 50 is created as a one-stop resource for investors who are looking to gain exposure to this exciting and growing market.
Our index provides investors with a curated set of 50 high-growth companies across different stages of funding and growth in their lifecycle.
Key Insights:
This report provides deep insights into each company’s performance based on alternative data metrics like website traffic growth, employee growth, product ratings, and many others available on the Synaptic platform.
Download the complete report now!
Big Tech: Competing across code-writing “Copilots” and proprietary foundational models
Unicorns: Building, acquiring and partnering to add AI features in their core tools
Unicorns are adding AI-based features into their tools through in-house development (e.g., Replit's Ghostwriter, Sourcegraph's Cody), acquisitions (e.g., CircleCI's Ponicode, Datadog's Codiga), and strategic partnerships for distribution and joint development (e.g., Replit-Google, Huggingface-ServiceNow).
Startups: Building for niche use cases and downstream automation
While some startups are competing head on with Copilot with their own coding assistants (e.g. Magic, Tabnine, BlackBox, Codeium), a lot of startup innovation is focusing on -
Individual Developers: Building quality-of-life Improvements
A highly-engaged community of Indie devs are building small open-source projects, typically wrappers on top of foundation models to add quality of life improvements (e.g. improving AI workflows within a specific IDE, AI assistants for very specific tasks like SQL queries etc.)
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Funding for new, AI-native coding tools has been fairly slow, especially when compared to the big deals in AI Copywriting Tools (Jasper - $125M, Tome - $75M, Typeface - $65M). Investors have rather doubled down their bets on Unicorns and Big Tech (e.g. Replit’s $100M raise, OpenAI’s $300M raise). Even some of the higher funded startups in the space like Diffblue, Swimm, Tabnine are tools that were started before the AI revolution, and adopted AI-based features subsequently.
This trend is understandable given the dominance of GitHub Copilot in the space. Unlike copywriting where the first wave of AI-based tools were from innovative startups (the likes of Microsoft, Google, Adobe etc. entering the fray relatively later), Copilot was launched within a few months of GPT-3’s release. A combination of first-mover advantage and solid distribution(GitHub + VS code) propelled the market to gravitate towards it. The demise of Kite, a promising code-writing assistant with $17M in funding, provides a case in point for investors being hesitant to fund startups competing head on with Copilot, Big Tech / Unicorn tools.
VC bets on startups in the space have thus been in startups serving niche use cases (e.g. Warp, Fig providing AI code assistance for command-line interface) or specializing in downstream developer activities (e.g. CodiumAI, Replay, Moderne for automated testing / bug identification; ReadMe, Mintlify for automated documentation).
Let’s take a look at the startups in the space, through an Alternative data lens.
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GitHub activity tends to be a strong indicator of traction, specifically for developer tools, as GitHub is the primary platform where developers interact with tools and contribute to open-source projects. A majority of AI-Coding Tools have taken open-source approaches to rapidly innovate on their products. GitHub stars (similar to “follow” on social media) are a direct indicator of a project’s popularity on GitHub.
Fig, Redocly, Tabnine, Warp. and Cursor have very popular repos on GitHub (for reference <0.002% of repos on GitHub have >10K stars). Cursor, Warp and Fig have the fastest growing repos among these tools (~10,500, ~2,200 and ~1,100 stars added in the last quarter respectively), followed by Redocly and Mintlify.
Cursor, an OpenAI funded startup which launched an AI-based Integrated Development Environment (IDE) this year in March, is seeing remarkable traction and reached 10K stars by end of March. Its value-proposition is natural AI-based workflows within the IDE (e.g. chatGPT-style code chat that can access your code, ability to see inline difference of AI edits, auto-debug by hovering over errors etc.).
Warp and Fig provide AI coding solutions for Command Line Interfaces (CLIs), a niche use case where developers are seeing value. Given CLIs are used widely, if intermittently, among developers for various tasks (e.g server administration, remote server operations, version control, scripting etc.), these startups seemingly have a strong platform for scaling their solutions.
Redocly and Mintlify are emerging as popular solutions for documentation. Redocly especially is standing out - with no publicly announced funding to its name.
Tabnine is a well-established code-assistant (launched in 2013) that has been able to hold its own vs Copilot by offering code privacy, local hosting, broad language support, multiple IDE integrations and ability to train the model on your own codebase. It will be interesting to see how it fares as the race for coding assistants heats up among Big Tech.
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Website visits tend to be a good indicator of traction for most SaaS businesses, including developer tools, as they are typically accessed through the startup’s website.
Apart from the tools discussed earlier, BlackBox comes through with strong traction on their website. BlackBox provides a GitHub Copilot alternative, that offers unique features like code search (without leaving the coding environment), code extraction from videos, broad language support and multiple IDE integrations. Like all coding assistants, the challenge for BlackBox lies in maintaining its traction against Big Tech offerings as competition intensifies.
Cursor’s website visits also validate the superb traction indicated by GitHub data.
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Furthermore, 2 macro factors are worth keeping an eye on, given their potential to significantly impact market evolution:
2023 is shaping up to be a pivotal year for AI coding tools. The stage is set for intense competition among Big Tech companies, exciting innovation from startups, and potential regulatory shifts. One thing is clear - coding is faster and easier than ever before.
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Private businesses are always on the cutting edge, innovating and changing faster than the market can anticipate. Investing in private companies is exciting but also challenging due to the constantly evolving nature of their business models and the limited availability of information. To help investors make informed decisions that can lead to profitable ventures, taxonomy helps classify companies based on their unique characteristics, industry affiliations, and business models. With this tool at their fingertips, investors can quickly spot the right investments to deep dive into, pinpoint rivals to keep an eye on, and stay ahead of relevant competitors.
For instance, an investor interested in early-stage K-12 EdTech companies can utilize detailed classifications to quickly find companies that meet their specific criteria. This helps investors objectively compare and benchmark companies within the same industry.
Business taxonomy, therefore, becomes the bedrock of investment analysis, providing a structured framework to categorize and understand companies based on their unique characteristics.
Because private companies operate in rapidly evolving industries with unique business models, the challenge arises in developing adaptable taxonomies that keep pace with frequent changes in private business models. Especially in the realm of startups, investors face obstacles stemming from limited information availability and the absence of global standards, as well as the subjectivity of classification, potentially leading to misrepresentations or under-representations of unique business models. For instance, classifying Stripe only as a specialized finance company fails to convey its core business proposition. Stripe has evolved from finance to a payment gateway and now also provides banking as a service.
Public companies are easier to understand because they must disclose their operational and financial information to regulators. On the other hand, private companies are not required to disclose the same level of information, leading to limited available data about their operations. Public investors have a smaller pool of companies to choose from, which allows for broader classifications. However, for private companies that are constantly evolving, this broad, sub-optimal classification could lead investors to miss important details when identifying investment opportunities.
Private market investors often turn to popular classification systems followed by major industry players like Pitchbook, and CB Insights. These players focus on industries, verticals, and keywords to map a company by going up to 2-3 levels deep into their business models.
This is a great starting point for investment research. However, the following are needs that emerge from these systems:
Granularity:
Investors look for deeper classification models that help them with granular information about a company’s current and emerging business models.
Adaptability:
Startups operate across business models that are continually evolving. Investors want classification models that can be updated frequently and reflect these emerging business models with new industry tags.
Up-to-Date:
Investors seek the latest information on a company’s current line of business and industry affiliations. With startups rapidly changing their business models and operations, classifications need to be capable of promptly tracking and reflecting these changes in company tagging.
Connected:
Traditional taxonomies rely on a keyword-based approach that fails to capture the connected view of industries and sub-industries. Investors look for a connected approach to taxonomy.
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At Synaptic, we deploy a proprietary taxonomy to overcome the challenges with the current systems. We allow you to go up to 10 levels deep into business models to capture all possible areas in which a company operates. Below are the core principles that underline our classification system:
#1 Dynamic business model mapping:
As private companies innovate and develop new business models, Synaptic refreshes itself to classify them into newer and more relevant categories.
#2 Multi-level classification:
Synaptic goes up to 10 levels deep into business model classification to provide granular information on a company’s area of operations, as illustrated in the figure below.
#3 One-to-many mapping:
One company operating across multiple lines of business is mapped to multiple categories instead of a single industry or vertical.
Let's comprehend the above principles using an illustration of the hierarchical taxonomy approach using Stripe.
Figure: Synaptic classification of business models mapped to Stripe.
Stripe was launched to simplify payment processing for businesses and individuals by providing an intuitive payment platform. The company's founders recognized challenges in the existing payment infrastructure and developed a platform that could be easily integrated into any website or mobile app using just a few lines of code, facilitating online payment acceptance.
Consequently, Stripe falls under the Payment Gateway category (Financial Industry > FinTech > Payments > Payment Gateway) in traditional taxonomies. As the company expanded its services over time to include invoice management and banking-as-a-service, Synaptic's modern approach allowed for multiple mappings to capture all five functionalities of Stripe in various categories.
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A well-designed taxonomy system enables investors to organize and prioritize potential investments, streamlining the process of identifying promising opportunities.
Deal sourcing:
By implementing comprehensive company classification, investors can quickly filter and sort through large amounts of data to focus on the most promising investment opportunities and save time. This ensures that they can identify and act on potential deals sooner.
Figure: Leveraging the Synaptic filters on comprehensive classifications to source edtech companies in North America
Competitive benchmarking:
A reliable business classification system provides the primary advantage of allowing investors to compare similar investment opportunities. With an inadequate classification system, investors may encounter comparisons that are either too broad or misaligned between companies with different business models.
For example, comparing Uber to a corporate taxi service is not an effective strategy for determining which company to invest in because their fundamental business models are different. Similarly, comparing Netflix to a traditional media company like Warner Media is inaccurate.
Figure: Similar companies suggested by Synaptic for competitive benchmarking
Market intelligence:
A comprehensive business classification system also aids investors in recognizing patterns and trends within the market. In-depth analyses of industry hierarchies can reveal emerging business models and trends, increasing the chances of investment success.
Figure: Employee counts for major companies in the online streaming services to evaluate hiring trends
76:T7ce,“By 2025, more than 75% of VC investor reviews will be informed using AI and data analytics.” - Gartner
Investors’ most-awaited 2023 highlights are here!
Learn how leading venture capital (VC) investors leverage data and artificial intelligence (AI) to become more productive, identify the most promising investment opportunities at the right point in time, and gain unique insights to steer their portfolio companies to success.
This first-of-its-kind report by Data-driven VC includes exclusive data on 151 VC firms, 65 thought leaders, 400+ tools, and a range of hands-on guides explaining how you can become more data-driven too!
In the report, you will learn:
Why should VCs become more data-driven?
Market Benchmarking
How to become data-driven?
You can also find the most frequently used tools below and the full list on this Notion page (including all data) here. According to the report, the leading data-driven VC firms are thoughtfully balancing the use of external off-the-shelf tools and the development of unique components in-house. Only while keeping their secret sauce, the core of their tech stack in-house, VC firms will be in a position to generate alpha in the long run. For a thorough analysis, download the report today.
Product reviews across the board are highly positive - which is typical for new ground-breaking use cases, early in the adoption cycle. Customer feedback tends to moderate as customer expectations increase with increasing adoption and product improvements. Jasper, WriteSonic and CopySmith have built strong customer franchisees (70%+ NPS), while CopyAI lags relatively.
Jasper and WriteSonic have the most complete, feature-rich products in the market - going beyond table-stakes like generative support for multiple content types (articles, blogs, emails, landing pages etc.), SEO optimization, multi-language support, content analytics to delight features like chatbots for research, chrome extensions, image generation, API access. Copysmith, through the acquisition of Frase, has one of the most liked product on the market, however it lacks the feature set of Jasper and WriteSonic (no text-based assistant like ChatSonic or Jasper chat, no support for image generation etc).
A deep-dive into text reviews reveals some clear themes across these products -
Jasper - apart from its strong feature set, customers highlight Jasper’s out-of-the-box integrations (e.g. Surfer SEO, Grammarly), automated / customizable workflows, reporting / analysis tools and security / access controls as key drivers of positive feedback. Themes typical of Enterprise-grade products.
WriteSonic - differentiates by offering a strong feature set (matching that of Jasper’s) at a more attractive lower-end price and offering best-in-class ease of use (through a large library of templates for different use cases and in-depth guides / how-to’s). A combination that sets it up really well in the individual & SMB segments of the market.
Copysmith - Frase stands out as a niche product among SEO-focused marketers. It’s top 20 analysis feature, which identifies the topic of your content and compares it to the top 20 search results in that topic is a major hit among customers. It remains to be seen how Copysmith can bring together Frase, Rytr and its own offering to create differentiation (as of now, all 3 tools operate independently).
CopyAI - lags competitors primarily due to a lack of performance on the core content generation use case. Customers highlight that content generated sometimes misses the mark, there are issues with writing long content (needs to be generated in multiple iterations, paragraph by paragraph vs in 1 go) and cases of duplicate results.
78:T43a,Investors use a wide range of datasets for a comprehensive analysis of companies they want to track. However, data availability about companies’ performance is limited to financial statements, regulatory filings, or management calls with analysts. Companies today leave behind a vast digital footprint as they conduct their business, providing signals on employees, customers, technologies, and more. This alternative data offers a 360-degree perspective on their performance, providing early and actionable insights on their growth.
In this whitepaper, you will learn about:
1. Alternative datasets available on Synaptic
2. How alternative data can be used to source and track new companies, compare companies with their competitors, and to spot growth indicators
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Even a free hand tracing of the Hype cycle wouldn’t be as strikingly close to it as the AI GitHub stars trend. Since the tempered growth in Q1’23, developer interest has grown and stabilized, entering the “slope of enlightenment” – where value-driven innovation grows. Serious developer engagement (i.e. GitHub contributors) in open source AI has continued to increase in 2024.
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[1] Source: Data-driven VC landscape 2023 by Dr. Andre Retterath
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Synaptic is a prominent player in the alternative-data-based investment ecosystem with a far superior taxonomy. It not only saves investors hours of effort but also offers them a considerably more diverse and informed choice of companies for each sector.
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revolutionized AI with its remarkable ability to understand and generate human-like text, triggering a wave of startups exploring use cases from chatbots and personal assistants to medical diagnoses and financial analysis. AI Copywriting was one of the first prominent use cases and has drawn significant excitement from investors, startups, and customers alike. The market has evolved at breakneck speed - in the last 6 months, we have seen the first unicorn in the space (Jasper), some big rounds, and big tech giants launching sweeping AI functionality that includes copywriting (Adobe, Microsoft). Here is an introduction to the market and the key things to look out for in 2023.
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![\"copywriting](\"https://ethical-cheese-5d63232b27.media.strapiapp.com/copywriting_1_1d3a5472a3.png\")
To scope the market, we considered tools specifically focused on generating marketing copy - excluded general writing tools (e.g. Sudowrite, Quilbot etc.) and tools focused on adjacent use cases like website personalization, sales outreach, email generation (e.g. Mutiny, Omneky, Persado, Unbounce, Regie.ai etc.). Website traffic and product review count are good indicators of adoption for SaaS products, especially those targeting a broad spectrum of customers including individuals and SMBs, which in this case is true for all the tools.
![\"copywriting](\"https://ethical-cheese-5d63232b27.media.strapiapp.com/copywriting_2_1c30e02f1e.png\")
While Jasper and CopyAI have been the most visited historically, WriteSonic has gained strong traction over the last few months - largely driven by the launch of its search-enabled chatbot ChatSonic, which garnered significant media attention. WriteSonic is likely to get another fillip in traffic from its own little moment at the Oscars - a controversial debunk of the Oscar-winning documentary Navalny, is claimed to have been written using WriteSonic.
![\"copywriting](\"https://ethical-cheese-5d63232b27.media.strapiapp.com/copywriting_6_02346ca20e.png\")
Team sizes data provides a stark contrast - Jasper has used its large funding to rapidly scale up their team. The purpose is clear - Jasper launched their “Jasper for Business” offering this February and is significantly bolstering its Sales and Engineering teams to prepare for the high-touch sales and product customization demands of Enterprise. WriteSonic’s team size, puts into context what an amazing job they have done to keep pace with Jasper on features, top of the funnel traction as well as customer experience.
![\"copywriting](\"https://ethical-cheese-5d63232b27.media.strapiapp.com/copywriting_5_ec678287d6.png\")
![\"copywriting](\"https://ethical-cheese-5d63232b27.media.strapiapp.com/copywriting_3_c4e20deaa4.png\")
Synaptic provides actionable insights from alternative datasets on a no-code platform, so you can invest with data-driven confidence. Synaptic unifies billions of data points from both your favorite data sources and our own proprietary sources in a single pane of glass, so you never miss out on spotting emerging trends and high-growth companies.
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In this whitepaper, we describe different types of smart alerts available on the Synaptic platform and highlight how they facilitate the discovery of novel and actionable data points.
Synaptic provides actionable insights from alternative datasets on a no-code platform, so you can invest with data-driven confidence. Synaptic unifies billions of data points from both your favorite data sources and our own proprietary sources in a single pane of glass, so you never miss out on spotting emerging trends and high-growth companies.
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In this whitepaper, we talk about:
- The importance of unifying data
- How Synaptic unifies diverse alternative datasets
- Insights that can be drawn from this data
![](\"https://synaptic.com/images/download-report.svg\")
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Synaptic provides actionable insights from alternative datasets on a no-code platform, so you can invest with data-driven confidence. Synaptic unifies billions of data points from both your favorite data sources and our own proprietary sources in a single pane of glass, so you never miss out on spotting emerging trends and high-growth companies.
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