Four layers, read bottom-up: tools ingest raw material, the registry connects and enriches it into one interlinked graph, each app formats it for its audience — and on top, you just ask.
Research sessions surface companies, people, and intel.
Episodes and transcripts mined for topics, themes, mentions.
The engine — classifies and routes everything sent to it.
Inbound mail mined for entities, intel, and context.
Everything else stripped away. One layer, one job: every piece of material that arrives makes some entity — or some theme — richer than it was.
A doc arrives saying Redpoint led a round in a company. The company doesn’t exist yet → a record is created, with the mention and what was discussed. Redpoint’s record gains the investment — its portfolio grows. And when a Redpoint partner is named alongside a company we track, that association lands on the partner, the firm, and the company at once.
The data layer, execution view — read bottom-up. The real work is at the base: AI extracting each document properly. Extraction feeds the tags and entity matches, and those feed the writing. All three stages are new builds — each reuses a live piece, but none of them exists yet. Nothing above can be better than the extraction underneath it.
records get richer
✓ reuses the live confirm queueconnect what was extracted
✓ reuses live record linkingAI reads every doc properly
✓ reuses the doc ingest plumbingA code audit of all seven repos changed the estimate. Most of the extraction already exists — newsletters, deals, facts, podcast entities, even the email door. What’s genuinely missing is the shared spine: one place where mentions, relationships, and typed tags land, and the wiring that points the existing machinery at it. Only step 1 is strictly sequential — once the spine exists, 2 and 3 run in parallel, and 4 lands last. Mostly integration, not new AI.
For a while we will deliberately have more than one visualization of the same thing. The existing DealFlow company view keeps working; a new Terminal view — say, initiated by a new venture deal appearing — lives beside it. Both sit above the data layer, both carry the same details and classification, and DealFlow simply shows a badge that a second view exists. Same pattern for people.
Built whenever it earns its place — plugs in above the record without touching it. Old views retire when the new one wins.
The LP Flow contact view keeps working. When a deal names the same person — a partner leading a round, a founder we start tracking — a Terminal view appears beside it. Both sit above the data layer, both carry the same details and classification, and LP Flow shows the badge that a second view exists.
A better person profile plugs in above the record whenever it earns its place — the record never changes.
The material the extraction pipeline works on. Every area flows through the same Stage 1 extraction — one pipeline, evaluated per document type — and lands on the same records, threads, and tags.
Most of the pieces below already exist as capabilities. The hook is what’s new: an automatic trigger, a filter, and no button to press.
Same four layers as the map. The middle layer is one thing — the data layer — and most of it is already in production. It gets the room here.
A doc arrives saying Redpoint led a round in a company. The company doesn’t exist yet → a record is created, with the mention and what was discussed. Redpoint’s record gains the investment — its portfolio grows. And when a Redpoint partner is named alongside a company we track, that association lands on the partner, the firm, and the company at once.