AI Stack for VCs
By Phil Boyer
About this collection
## AI Tools Transforming Venture Capital Operations This collection reveals how AI is fundamentally reshaping venture capital operations across the entire investment lifecycle. VCs are rapidly adopting specialized AI tools for **five core functions**: deal sourcing and lead generation (Harmonic, Tracxn, Grata), relationship management and CRM (Affinity), portfolio monitoring and performance tracking (Standard Metrics), meeting documentation and note-taking (Granola, Fireflies.ai), and valuation management (Derivatas). The transformation spans from **automating manual tasks** like data entry and startup screening to **enhancing strategic decision-making** through predictive analytics and real-time portfolio insights. Leading firms are already leveraging these tools to manage thousands of portfolio companies more effectively. Many VCs are using **specialized, off-the-shelf solutions** while also building in-house capabilities, with tools offering AI-powered automation, relationship intelligence, and integrated workflows becoming essential competitive advantages. The shift represents a move from spreadsheet-based operations to sophisticated, data-driven investment management platforms that enable faster decision-making and better LP reporting.
Curated Sources
Gopi's Manifesto - Ensemble VC
Gopi Sundaramurthy, Founder and Head of Data Science at Ensemble VC, outlines a new model for investment firms in the age of AI. The article argues that most venture firms are superficially adopting data-driven approaches, but true transformation requires reorienting their entire workflow around data and AI. The proposed model involves a layered, machine-augmented process where machines handle lower-level assessments, filters, and prioritizations, freeing human decision-makers to focus on higher-order problems. The article presents a blueprint for building a truly data-oriented firm, with four levels of maturity: Traditional, Data Edge, Process Edge, and Strategic Collaboration. As firms progress through these levels, they achieve compounding rewards, including faster and more informed decision-making, improved efficiency, and enhanced collaboration. The key to success lies in shifting the mental model of how investment work gets done and investing in customized data infrastructure.
Key Takeaways
- The true advantage of AI in venture capital lies not in making 'the right decision,' but in the thousands of lower-level assessments and prioritizations that shape what gets surfaced for human decision-makers.
- Firms that adopt AI-driven data platforms early will experience a compounding advantage, learning from their own activity and improving faster than their peers.
- The journey to becoming a data-oriented firm involves a series of steps, from adding technical capability to rebuilding the team and eventually achieving strategic collaboration with external stakeholders.
How to establish a data strategy for your fund - Harmonic.ai
Gopi Sundaramurthy, Co-founder and Head of Data Science at Ensemble VC, discusses how the firm has developed a data strategy to enhance its investment process. Ensemble VC has built a data-driven model that prioritizes potential investments, reducing the ratio of companies reviewed to investments made from 100:1 to 5:1. The firm uses a sprint cycle process, where investors validate leads, track progress, and provide feedback that informs the model. The data strategy has improved the efficiency of the investment team and allowed them to focus on high-potential companies. However, Sundaramurthy notes that while data is useful for sourcing and screening, human evaluation is still essential for assessing the value of companies.
Key Takeaways
- Ensemble VC's data strategy has significantly reduced the time spent by investors on qualifying deals, achieving a 5:1 ratio of companies reviewed to investments made.
- The firm's sprint cycle process has improved the efficiency and repeatability of the investment process, allowing investors to focus on high-potential companies.
- While data is crucial for sourcing and screening, human evaluation remains essential for assessing the value of companies and making investment decisions.
Market Map: Data + AI Tooling for Venture Capital Firms
This document provides a comprehensive market map of 130+ data and AI tools for venture capital firms, supporting the VC AI Tooling 5x5 framework that outlines 25 high-impact use cases across sourcing, investing, portfolio management, internal operations, and network activation. The map aggregates tools that help VC funds implement strategies such as AI agents for spotting emerging founders, automated investment memo drafting, and fund return simulation. The author, Cory Bolotsky, positions data and AI as key to transforming VC firms into more efficient and scalable operations, with platform leaders becoming de facto Chief Product Officers. The market map is presented as a dynamic snapshot, acknowledging the rapidly evolving landscape of data and AI tooling for VC firms.
Key Takeaways
- The integration of data and AI tools is transforming venture capital firms by enhancing their operational efficiency and scalability, with platform leaders taking on a product-centric role.
- The VC AI Tooling 5x5 framework offers a structured approach to identifying high-impact applications of AI and data across various VC firm functions, from sourcing to network activation.
- The market map highlights the breadth of available tools, from AI-driven founder discovery to automated investment memo generation, providing VC firms with a resource to inform their technology adoption strategies.
Building a personalized VC Copilot – Yohei Nakajima
The blog post discusses the author's vision for an AI-first VC firm and shares their experiments with various AI tools to enhance different aspects of venture capital operations. The author describes their experience with tools for networking, deal sourcing, due diligence, portfolio support, and fundraising prioritization. They also provide a step-by-step guide for VCs to get started with AI, ranging from beginner to advanced levels, and emphasize the importance of building a culture that encourages experimentation with AI tools. The author shares their own experiences of building custom tools and highlights the potential for AI to significantly improve VC operations.
Key Takeaways
- The author envisions an AI-first VC firm where AI handles general tasks, networking, deal sourcing, due diligence, portfolio support, admin and reporting, and fundraising, thereby significantly enhancing operational efficiency.
- Experimentation with AI tools is crucial for VCs to understand their potential and limitations, with the author having built over 100 tools and encouraging others to do the same.
- Building a culture that supports AI experimentation within VC firms is more important than the tools themselves, as it enables teams to effectively leverage AI for their specific needs.
(7) AI that was inevitable - by Jamesin Seidel
The author discusses Google Sheets' new AI feature, allowing users to call prompts directly from cells. This development was inevitable and streamlines workflows that previously required complex workarounds involving LLM APIs. The author shares a real use case where they used the AI feature to analyze AI funding rounds for a portfolio company, simplifying the process from data collection to categorization. The integration has significant implications for workflow automation, particularly for horizontal solutions, and highlights the dominance of vertical plays in the venture market unless focused on core AI infrastructure.
Key Takeaways
- The integration of AI in Google Sheets signifies a broader trend where tech giants enhance existing tools with AI, making horizontal workflow automation challenging for other companies.
- The new AI feature in Google Sheets simplifies complex workflows, such as data analysis and categorization, previously requiring multiple steps and API calls.
- The development underscores the importance of vertical plays in the venture market, as opposed to general horizontal workflow solutions, unless the latter are focused on core AI infrastructure.
How Venture Capital Funds Can Leverage AI To Save Time, Cut Costs, And Boost Returns
The article discusses how venture capital (VC) firms can leverage artificial intelligence (AI) to improve their operations, enhance decision-making, and boost returns. It highlights various AI tools and strategies that VCs can use to streamline deal sourcing, automate initial screening, personalize outreach, and improve portfolio management. The article emphasizes that AI is not just a productivity tool but a thought partner that can help VCs think differently and make better investment decisions. Christian Ulstrup, founder of Accelerated AI Adoption, shares insights on how AI can help VCs reclaim time, improve decision quality, and gain a competitive edge. The article provides practical next steps for VC firms to adopt AI tools, including starting with high-ROI use cases, creating standardized prompt templates, and establishing oversight. It also notes that the most sophisticated funds are developing proprietary AI systems to codify their unique investment philosophy and expertise.
Key Takeaways
- The integration of AI in venture capital can lead to significant efficiency gains and improved decision-making, with early adopters reclaiming 1-2 hours daily and enhancing decision quality.
- AI tools can be used across various stages of the VC process, from deal sourcing and initial screening to personalized outreach and portfolio management, allowing for more strategic use of time.
- The true value of AI in VC lies not just in efficiency but in its ability to act as a thought partner, helping firms think differently and identify non-obvious risks and opportunities.
- To effectively implement AI, VC firms should start with high-ROI use cases, develop standardized prompt templates, and establish appropriate oversight to ensure quality and accuracy.
- The future of VC-AI partnership involves developing proprietary AI systems that codify a firm's unique investment philosophy, potentially creating a sustainable competitive edge.
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
This study explores the application of large language models (LLMs) in venture capital decision-making, focusing on predicting startup success based on founder characteristics. The authors utilize LLM prompting techniques to generate features from limited data, extract insights through statistics and machine learning, and demonstrate the effectiveness of these characteristics in prediction. The results reveal potential relationships between certain founder characteristics and success, with implications for VC firms seeking to optimize their investment strategies.
Key Takeaways
- The study demonstrates the potential of LLMs in predicting startup success by analyzing founder characteristics, with machine learning models achieving relatively high precision.
- The research highlights the importance of considering various founder attributes, such as entrepreneurial experience, education, and background, in assessing startup potential.
- The findings suggest that certain founder characteristics, like military background and notable awards, are associated with higher success rates, while others, like experience in nonprofit organizations, are linked to lower success rates.
- The study's limitations include the individual-level analysis of founders, neglecting co-founder interactions, and potential inaccuracies in LLM outputs, which should be addressed in future research.
Best VC Tech Stack Tools in 2025: Portfolio Monitoring & More | Standard Metrics
The document outlines the top tech stack tools for venture capital firms in 2025, focusing on portfolio monitoring, deal management, lead sourcing, meeting notes, and valuation software. Standard Metrics is highlighted as the best portfolio monitoring platform, offering real-time data and AI-driven financial parsing. Affinity is identified as the top deal management CRM, while Harmonic excels in lead sourcing. Granola is praised for its meeting notes capabilities, and Derivatas is recognized for its valuation software. The document emphasizes the importance of automated, VC-first, and collaborative tools in reducing friction across the fund lifecycle.
Key Takeaways
- The top VC tech stack tools in 2025 prioritize automation, VC-specific design, and collaboration to streamline workflows and improve investment decisions.
- Standard Metrics and Derivatas demonstrate the value of integrating portfolio data and financials to enhance reporting and valuation processes.
- The highlighted tools collectively address key pain points in VC operations, from sourcing deals to managing portfolio data and reporting to LPs.
How AI Tools are Reshaping Venture Capital: Tools to Know - Visible.vc
The venture capital industry is undergoing a significant transformation with the integration of AI tools, enhancing deal sourcing, due diligence, and portfolio management. AI technologies automate labor-intensive processes, provide deeper insights, and enable more informed decision-making. Key AI tools being used include Visible AI Inbox, Caena, Merlin, Tracxn, TechScout, and Quid, each offering unique capabilities such as deal flow automation, startup evaluation, and market trend analysis. These tools are not only streamlining operations but also providing venture capitalists with a competitive edge in identifying high-potential startups and managing investments more effectively.
Key Takeaways
- The integration of AI in venture capital is revolutionizing the industry by automating deal sourcing, enhancing due diligence, and improving portfolio management.
- AI tools such as Caena, Tracxn, and TechScout are being utilized to analyze vast amounts of data, providing insights into startup viability, market trends, and competitive landscapes.
- The use of AI in venture capital enables investors to make more informed decisions, mitigate risks, and capitalize on emerging trends and technologies.
10 AI tools transforming venture capital: The new VC tech stack essentials
The document discusses the impact of AI on the venture capital industry, highlighting its role in automating deal sourcing, improving decision-making, and enhancing portfolio management. It lists 10 AI tools used by venture capital firms, including Affinity, ChatGPT, and Quid, and explains their applications in deal flow automation, data entry automation, startup evaluation, and deal negotiation. The use of AI is shown to increase operational efficiency, provide real-time insights, and support data-driven investment decisions. The number of data-driven firms in the venture capital industry increased by 20% from 2023 to 2024, indicating a growing adoption of AI technologies.
Key Takeaways
- The adoption of AI tools in venture capital is becoming essential for firms to remain competitive, with a 20% increase in data-driven firms from 2023 to 2024.
- AI is transforming key aspects of venture capital operations, including deal sourcing automation, portfolio management, and deal negotiation, enabling more informed and efficient investment decisions.
- The use of off-the-shelf AI tools is preferred over building in-house solutions for many VC firms, providing an integrated and cost-effective approach to managing deal flow and portfolio companies.
"What AI Tools do VCs use? - A Taxonomy" - Help me uncover how VCs use AI in the investment process for my master’s thesis! 🚀💡 : r/venturecapital
The document is a discussion thread on the subreddit r/venturecapital where a master's student is seeking insights on AI tools used by Venture Capitalists (VCs) throughout the investment process. The student is developing a taxonomy of AI applications used by VCs, similar to a framework by Weking et al. (2020). The thread includes a list of AI tools already identified and invites VCs to share their experiences with AI tools, including their usage, benefits, challenges, and future AI strategies. The discussion reveals various AI tools used by VCs for deal sourcing, due diligence, portfolio management, and other tasks, such as Affinity, Airtable, Altvia, Attio, ChatGPT, and Make.com. Participants share their preferences for certain tools, reasons for abandoning others, and the benefits and challenges they face when implementing AI solutions.
Key Takeaways
- The use of AI tools is widespread among VCs, with applications in deal sourcing, due diligence, and portfolio management.
- VCs use a variety of AI tools, including third-party applications and custom-built solutions using APIs.
- The key benefits of AI tools for VCs include improved speed, efficiency, accuracy, and collaboration, while challenges include integration issues, cost barriers, and lack of functionality.
Frequently Asked Questions
- How do the relationship intelligence capabilities of Affinity compare with the deal sourcing automation of Harmonic and Tracxn in terms of actual deal closure rates?
- What specific integration points exist between Standard Metrics' portfolio monitoring and Derivatas' valuation software, and how does this affect quarterly reporting accuracy?
- How do AI-powered meeting tools like Granola and Fireflies.ai change the dynamics of partner meetings and IC decisions compared to traditional note-taking methods?
- What are the key differences between VCs who build custom AI solutions using APIs versus those who rely exclusively on third-party tools like the ones mentioned?
- How does the Global Benchmarking feature in Standard Metrics influence investment decision-making compared to traditional market research methods?
- What workflow automations can be created by combining Make.com with tools like Affinity, Standard Metrics, and Granola across the entire deal lifecycle?
- How do the AI-driven financial data parsing capabilities of Standard Metrics compare to manual data collection in terms of accuracy and LP reporting quality?
- What specific 'stealth signals' does Harmonic identify for early-stage startup detection that traditional databases like PitchBook and Crunchbase miss?