ResearchTechVC matchmaking

Startups stop pitching investors who were never a fit.

01 · The problem

Startups pitched investors who were never a fit.

Knowing who fits takes deep research on each fund.

02 · What I built

I built a pipeline that researches every VC fund.

Then a system that runs for each startup, every time.
An AI agent interviews the startup in Claude or Codex.
It learns what the startup values and needs, and studies its market.
It checks every fund for fit, and ranks them all.
The startup sees where to start: a partner, the most money, contacts or infrastructure, whatever it needs.

03 · How it looks

One startup's top matches, each with why it fits and a source.

Matches for Brightline · seed, B2B payments

01Northfield CapitalLeads seed rounds in B2B fintech. Two partners focus on payments.northfield.example
02Harbor Lane VenturesInvests the amount you're raising. Backed a similar company in 2024.news.example
03Ostrava SeedInvests in founders in Central and Eastern Europe at this stage.ostrava.example

Made-up names.

04 · The result

5,000 VC funds, ranked for each startup, every time.

The startup gets the funds that fit, in order.

Check it yourself

See a real result

Next story

CredoraStablecoin research

Credora enters a stablecoin name. Their spreadsheet fills itself.

Before: Their team filled every field by hand.
I built: A pipeline that researches every field on their list.
Result: Every value comes with its sources and a confidence level.

Bring one research question.

Book a free discovery call

or email yuriy@littlebit.dev