How do VC funds build an edge when everyone has the same data?

The VC operations world is remarkably similar to the go-to-market space in SaaS; we see it firsthand at Extruct, where we have a good split of sales and investor roles among our users.

Founders receive nearly identical outreach messages from dozens of firms within hours of each other, all triggered by the same "coming out of stealth" post, the same TechCrunch funding announcement, or website traffic surge. The surface layer of data-driven investing has been fully commoditized.

In this piece, we explore how VC funds build a lasting edge when every competitor is buying the same data on the same companies.

Our Takeaways

1. We analyzed 152 VC/PE funds that have either product, data, or engineering FTEs; 116 have product roles, 44 have data roles, and only 7 have all three. Most funds are building products, not data pipelines.
2. Seed, Series A, and growth are effectively three different businesses; a data stack built for seed signal-hunting is useless for growth-stage NAV forecasting, yet most DDVC tooling ignores this.
3. The best seed funds don't just scrape data, they build proprietary signal networks (GitHub star graphs, domain registry monitors, TikTok trend scrapers) that compound over time.
4. Data helps you find deals, but not necessarily win allocation, interpretation, and relationships still matter more than raw data access.
5. AI has democratized the research layer, but the winners will be funds that build proprietary datasets, keep their CRM actionable, and synthesize insights no agent can replicate.

What is Data-Driven VC?

Venture capital has traditionally been a relationship business. Data-Driven VC (DDVC) is the movement to bring some structure to that. At its core, it means applying systematic, data-first approaches to the venture capital workflow, sourcing deals, evaluating startups, supporting portfolio companies, and making investment decisions.

Private markets lagged behind public ones because data was scarce, unstructured, and locked behind relationships. Over the past few years, that's changed. Alternative datasets, NLP, LLMs, and ML frameworks have made it possible to process the messy, fragmented information that defines the startup ecosystem.

Over the course of building Extruct, we've collaborated with many funds that excel at building data strategies:

• Some start with simple stealth founder identification.
• A deep tech fund maps scientific domains with a knowledge graph that helps to evaluate 4,000 startups per year through a systematic, scientific process.
• One fund maintains curated "seed accounts" on GitHub. They monitor which repositories these accounts star, then trace other accounts that were among the first to star the same repos, gradually expanding the list. This runs every six hours to build a ranked list of trending repositories within specific themes. It's not about tracking all of GitHub; it's about monitoring the topics that matter to the thesis.
• Some funds scrape domain registries to catch the newest .ai startups. Others monitor TikTok to spot viral trends relevant to prosumer products.
• Another fund is allocating $500k per year for data purchases to form a quantitative approach.

So what does the landscape actually look like today?

What data-driven VC funds actually look like

How do you tell which funds are actually data-driven versus just claiming to be? We used a simple proxy: hiring. If a fund employs product managers, data scientists, or engineers, it's investing in building technology, not just using off-the-shelf tools. We identified 152 funds that have at least one of these roles and mapped their teams across three categories: Product, Data, and Engineering/Automation.

Grab the full table CSV here.

Three asset classes, three ways to operate

VC is often treated as a single asset class, but in reality, it's three distinct asset classes that require entirely different operational lenses and approaches.

Seed (88 funds): Highest volume. Sourcing is about signal detection, founder identification, and casting a wide net. You're investing in and co-creating the company alongside the founder. The goal of DDVC here is to bring you confidence and find out if the market round is reasonably priced, and how to track its earliest inception.

Series A (70 funds): Funds shift toward data-driven evaluation of traction metrics, ARR, growth rates, and retention. CRM systems become more critical for tracking relationships from seed to A.

Growth / PE (24 funds): Tools shift to financial modeling, portfolio monitoring dashboards, NAV forecasting, and attribution analysis. Discipline and analytics over discovery.

Each stage demands a fundamentally different data strategy, different tooling, and human judgment.

How seed funds actually source: three archetypes

The seed stage is where data-driven approaches have the widest scope. We see four distinct archetypes:

a) Signal-hunting at scale

The "find them before anyone else" play. Say, SignalFire's Beacon AI platform tracks 80M+ companies and 600M individuals across 40 datasets. TRAC is 100% AI-driven, using predictive AI to scan market data and flag startups with traction before they raise. 645 Ventures built a proprietary "Voyager" platform that analyzes thousands of companies monthly, and over 50% of their seed deals progress to Series A. QuantumLight's Aleph AI system tracks 700K+ companies and analyzes 10B+ data points.

b) Founder and talent identification

The "bet on people" play. Entrepreneurs First pioneered "Talent Investing", assessing ambition and founder aptitude as quantifiable signals. Outlander VC uses a 38-point "founder framework" scoring founder-market fit and execution capability. N47 looks at qualitative signals like "user love," "builder instinct," "learning velocity," and "craftsmanship."

c) Thesis-driven domain intelligence

The "deep expertise" plays. Deep Science Ventures built an "Outcomes Graph", a knowledge graph mapping constraints and solutions across deep-tech sectors.

How to stand out when everyone has access to the same data vendors?

Data has become more affordable and accessible. A large number of funds don't need a strong data team today. Want stealth founder intelligence? There are plenty of vendors selling raw data and SaaS wrappers. Want to spot tier-1 investor interest in a certain company? Same story.

So how do you distinguish yourself here?

Every fund has its own thesis. The product that actually works isn't a dashboard with data; it's a system that aligns with your specific investment strategy. It filters the noise through the lens of what you actually believe in, by building a nuanced data product on top of the data layer, where data and technology become core to differentiation.

The surprising finding from the hiring data isn't about data engineers. It's about product managers. The product/operator role becomes especially interesting if the management fee can support it.

We looked at 163 product people across 116 funds and found three distinct archetypes:

Internal platform builders, PMs building the fund's own investment tech. SignalFire's Heath Black (ex-Reddit, ex-Facebook, holds AI patents) runs the Beacon AI platform. EQT has 11 product people, including an Engineering Manager for Motherbrain with a PhD in distributed multi-agent systems. Insight Partners has a Senior PM shipping their internal LLM platform. These are engineers with PM titles, AWS-certified, ML-credentialed, building real infrastructure.

Portfolio support leads, PMs serving the fund's startups. Sutter Hill's Luke Wroblewski advises portfolio companies on AI product design. TinySeed's Tracy Osborn (ex-founder) helps SaaS founders scale. Golden Ventures, Uncork Capital, and Launch Africa all have similar dedicated platform roles.

Product-as-a-service teams, full product orgs at fund scale. York IE has 11 PMs in Ahmedabad running advisory and product development. Cur8 Capital has a Head of Product & Engineering plus three PMs. Activant has three Senior PMs building e-commerce tools.

A few hybrid roles stand out: HarbourVest has a Quantitative Product Manager. Georgian's Qaid Damji runs Product & Analytics. HPI Ventures combines an Investment Manager with a Head of Data & Product.

The vibe-coding era makes this even more accessible. A fund with a couple of strong product builders can ship internal tools and lightweight applications that used to require a full engineering team.

But it's worth stepping back. Many successful funds operate without any of this. No product team, no data engineers, no internal platforms. They use Affinity or Attio, outsource specific projects as needed, and focus on deal sourcing, relationships, and portfolio support.

The decision to build should be driven by your fund's specific thesis and operational model, not by what the most technically ambitious funds happen to be doing.

The data itself was never the moat. Enrichment APIs, scraped databases, and company profiles are available to anyone willing to pay. What compounds is how a fund organizes, connects, and acts on what it learns, research that links across thesis areas, and the conviction to break from consensus when your own analysis says something different.

The bottleneck isn't finding the deal; it's clarity of thought. Data doesn't give you that directly, but it sets the conditions for it. The data compounds, and so does the thinking built on top of it.