How VCs source pre-seed deal flow from public data

When we launched Unbuilt Lab we did not expect VCs to subscribe. The persona was clearly founder-facing. But within six months, about 15% of our paid accounts were partners at sub-$50M funds, syndicate leads on AngelList, and full-time angel scouts. They use the product differently from founders — they are not hunting for an idea to build, they are hunting for problem-spaces about to produce a wave of foundable companies. The use case surprised us enough that we eventually wrote this guide specifically to document how they read the same data founders read.

This guide explains how those VCs use Unbuilt Lab as a passive deal-flow layer, what they look at in the 6-dimension score that founders skip, why public-data signals tend to lead inbound dealflow by 3-6 months, and how the workflow differs from the founder use case in concrete ways.

The thesis: public-data demand leads founder pitches

Founders are reactive. They notice a problem (on Reddit, in their day job, at a hackathon) and then take 3-9 months to incorporate, build a deck, build a prototype and start pitching investors. By the time a VC's inbox lights up with five pitches in the same space, that space has been visible in public discussion for at least six months.

Unbuilt Lab's catalog is a snapshot of those public discussions, organised by topic cluster, scored by demand-vs-gap, and refreshed daily. If a problem space is rising fast in public discussion right now, it is reasonable to expect inbound deal flow in that space to rise 3-6 months from now.

This is why some VCs use the catalog as a leading indicator. They are not trying to find a specific company. They are trying to see which topic clusters are accelerating so they can be the first call when a founder pitches in that space.

How a VC actually uses the catalog (workflow)

The typical VC workflow we hear back from paying subscribers is weekly, not daily. They block 30-45 minutes once a week to scan the catalog updates. Within that block they do three things.

First, they sort by trend score. The trend dimension specifically measures acceleration of public discussion over the trailing 90 days. Topics that scored 60 last month and 85 this month are flagged as fast-moving — these are where new companies are about to appear.

Second, they read the evidence snapshots on the top 5-10 fast-movers. Evidence snapshots are the actual Reddit threads, Quora questions, GitHub issues and Stack Overflow posts that triggered the cluster. This gives them the language users are actually using to describe the problem, which is what they will hear in pitches three months later.

Third, they tag the topics they want to be visible in. Some VCs do this by writing public LinkedIn posts about the problem space, others by adding the space to their fund's stated thesis, others by reaching out directly to founders already working in the space. The catalog is the prompt for those moves.

The dimensions VCs read differently from founders

Founders optimise for feasibility and monetization. VCs largely ignore those two and double down on demand and gap.

Demand score is asymmetric for VCs. If demand is high in a space they cannot fund (B2B-government, regulated healthcare, hard tech they do not have expertise in), they pass. But if demand is high in a space they can fund, they will accept ugly feasibility and weak monetization. A VC's job is to bet on a market, not a finished business.

Gap score (how poorly existing solutions cover the demand) is the single most-watched dimension for VCs. A wide gap means the problem is real but the market has not coordinated on a winning solution yet. That is the textbook moment to fund a contender.

Founders, by contrast, often prefer narrower gaps where a known solution exists and they only need to do it better. VCs almost never invest in those positions because the moat is shallow.

Common topic clusters that have been hot in 2025-2026

A short list of clusters we have seen sustained VC attention on:

• AI-native vertical workflow tools (legal, accounting, recruiting, sales operations)
• Developer tooling around AI agents and code-generation evaluation
• Personal-finance applications layered on top of open banking
• Climate-adjacent SaaS — carbon accounting, supply-chain emissions tracking
• Workforce-automation in non-tech blue-collar industries (HVAC, plumbing, landscaping)
• B2B compliance and trust-and-safety tools driven by EU regulation

Each of these clusters has multiple sub-clusters in the catalog with independent demand and gap scores. The VCs who use Unbuilt Lab tend to subscribe at the Founder or Studio tier so they can pull weekly diffs across all clusters rather than reading one at a time.

Three signals that consistently precede a wave of foundable companies

Across the catalog history we have noticed three signal patterns that reliably precede a foundable wave by 6-12 months. They are not the only signals worth tracking but they appear frequently in retrospect when you look at how a category became fundable.

The first is sustained Reddit and Quora question volume across more than 12 weeks. One-week spikes are noise. Twelve-week sustained climbs are signal. Most clusters in the catalog include the underlying weekly mention timeline so you can distinguish the two visually.

The second is GitHub issue creation in mature OSS projects asking for a feature the maintainers consistently decline. "Won't fix" issues with high upvote counts are unmet demand that someone will productize. Some of the biggest dev-tool companies of the last decade started exactly there.

The third is regulation-driven demand. When a new regulation (EU Digital Services Act, EU AI Act, California CCPA, Indian DPDP) shifts compliance obligations onto businesses, a wave of compliance-tooling companies follows within 12-18 months. Following regulatory calendars is one of the most under-rated VC research practices.

What we won't show you (and why)

One reasonable VC question is whether Unbuilt Lab will eventually surface specific founders or specific companies in those clusters. The answer is no. We deliberately do not ingest LinkedIn, Crunchbase, AngelList founder profiles or any other founder-identifying data. We index public discussion of problems, not the people working on them.

The reasoning is part product, part principle. Product-side, founders use Unbuilt Lab to discover ideas; they would not subscribe if we were also a sourcing tool for VCs scouting them. Principle-side, we think the right separation is for VCs to source founders through their own networks and use our data only for the upstream market-thesis layer.

If you need founder sourcing data, combine an Unbuilt Lab subscription with Crunchbase, Harmonic, or Sourcescrub. The combination works well; the overlap is zero.

Why this is not a Crunchbase or Pitchbook replacement

Important to be clear: Unbuilt Lab is not a deal-tracking tool, a portfolio-management tool or a competitor to Crunchbase. It does not list companies, founders, funding rounds or cap tables. Those tools answer the question "what has been funded." Unbuilt Lab answers a different question: "what problems is the market about to fund?"

The combination is what works. A VC uses Crunchbase to track what is happening, and Unbuilt Lab to anticipate where to look next. Some funds explicitly assign one analyst to the public-data layer (us, Reddit's r/startups directly, X bookmark scrapes, GitHub trending), and that analyst's job is to brief partners weekly on emerging clusters before pitches arrive.

How thematic VCs use catalog diffs week over week

One workflow that has emerged among thematic VCs (funds with a stated investment thesis like fintech, climate, dev-tools) is the weekly catalog diff. They subscribe at the Founder or Studio tier, set up a calendar block every Monday morning, and look only at clusters that have moved more than 10 points on either the demand or trend dimension over the previous week.

This produces roughly 20-40 clusters per week to skim, of which 2-5 are typically interesting enough to merit a deeper read. The 30 minutes spent on this routine generates more thematic insight than reading the previous week's tech-press headlines, because the catalog reflects what users are saying about their own problems rather than what journalists are saying about company news.

For partners who already have an analyst, the analyst typically owns this workflow and produces a one-page Monday brief that the partner reads with their coffee. That brief plus the partner's own network signals is the basis for the week's outbound outreach to founders.

Pricing for funds and angel scouts

VCs almost always subscribe at Founder ($29.99/month or $299.99/year) or Studio ($49.99/month or $499.99/year). The Studio tier includes seat-based access which is useful when a partner wants to share clusters with an analyst.

Funds occasionally ask for a custom enterprise tier with API access. That is on our roadmap but not generally available — we prioritise the founder-facing experience. If your fund has a specific data integration need, email hello@unbuiltlab.com. We have done a handful of custom data shares.

Sources & further reading

• a16z — state of AI in enterprise (cluster-level demand data)
• CB Insights — venture trends quarterly report