Private equity & VC
AI automation for private equity & VC firms
A fund’s network is its edge, and that network usually lives in five places at once: partner inboxes, event exports, CRM fragments, spreadsheets from the last fundraise. Merging them by hand fails on accuracy; uploading them to a random SaaS fails on GDPR. I build the third option: consolidation engines that run inside your own environment.
Where the money leaks
Contact lists scattered everywhere
Every partner, event, and fundraise produces another list. The same founder exists five times with three spellings and two employers, and nobody knows which record to trust.
Dedup by hand doesn’t work
Spreadsheet dedup catches exact matches and misses everything that matters: typos, name variants, the person who changed firms. Past a few thousand rows, interns and VLOOKUP stop being a plan.
GDPR rules out the easy fixes
The obvious shortcut, exporting everything into a third-party data tool, is often exactly what your compliance obligations forbid. Contact data of European founders and LPs has to be processed somewhere defensible.
What I build for private equity & vc
Contact consolidation with fuzzy dedup
Multi-list merge engines that match on more than exact strings: name variants, email patterns, derived company fields, with validation sampling so you can verify accuracy instead of hoping for it.
Pipelines that run inside your environment
When compliance requires it, the entire engine runs inside your own platform and the data never leaves it. I’ve built under exactly this constraint, not just talked about it.
Deal-flow and LP data hygiene
Empty fields enriched from your own source exports, QA checks that prove correctness, and a database structured for search, so “who do we know at this company” gets a real answer.
Proof, not promises
For a European venture-capital firm I built a GDPR-compliant contact-merge engine across 5 lists: fuzzy dedup, company derivation, validation sampling, running inside the client’s EU platform so the data never left it. The same discipline at larger scale: a database of 863,484 contacts, deduplicated to zero duplicates, with 1.2M+ empty fields enriched.
Read the contact-data engine case studyCommon questions
Our data can’t leave our environment. Is that a problem?
No, it’s a requirement I’ve already shipped against: the merge engine above was built to run inside the client’s own EU platform precisely so the data never left it. Where the system runs is a design input, not an obstacle.
How do we know the dedup is actually correct?
You measure it instead of trusting it: validation sampling on the merge logic and explicit QA checks on the result. The largest database I operate passed 8/8 QA checks and holds 863,484 contacts with zero duplicates.
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