Deep Company Dossier — Palantir
Deep Company Dossier · graph-native · point-in-time

Palantir Technologies

NYSE: PLTR · dossier as-of 2026-08 · built by walking the entity graph

Not fundamentals, not price. This is the layer underneath: who runs it, where they came from, who they're tied to, what they've done — the relationship dimensions of a company that a 10-K and a price feed can't give you, assembled as-of a date so it stays honest for what came next.

The dossier — 9 dimensions

Management pedigree school · prior companies
Live
Board network interlocks
Live
Past-company outcomes what happened to them
Live
M&A / deal activity last-year deals
Live
Backers / holders who funds it
Live
Partnerships who they work with
Roadmap
Products & launches shipped / announced
Roadmap
Customers & suppliers read-through
Roadmap
Competitors who they fight
Roadmap

The five "Live" dimensions are graph-native today; the four "Roadmap" ones are the commercial / product edges landing next (partner_of, product-event nodes, customer_of). The dossier deepens on its own as they arrive — no rewrite.

01 · Who runs it

A single pedigree runs the whole company

Walking Palantir's insiders to their studied_at + worked_at edges surfaces the tell instantly — this is a Stanford company, top to bottom, wired to the PayPal/Founders Fund network:

Peter Thiel · Chairman / founder-backer
School
StanfordStanford Law
Also at
Meta (board)AbCelleraChemomab
Alexander C. Karp · CEO / co-founder
School
StanfordHaverfordGoethe University Frankfurt
Shyam Sankar · CTO
School
StanfordCornell
Also at
Ginkgo Bioworks
Stephen Cohen · co-founder
School
Stanford

Four of the top insiders, four Stanford ties, plus Thiel's active board seat at Meta — a concentration of pedigree and network you'd assemble by hand over days, returned in one traversal.

Where this is going — the dossier is a feature vector

The point isn't the pretty page. It's that this dossier is structured, and it's point-in-time — every edge carries an as_of, so a dossier built "as-of the day before an earnings call" only contains what was knowable then. Generate one for every company before every event, pair it with the realized outcome, and you have a clean, non-leaky training corpus:

graph → PIT dossier (as-of event−1) → pair with realized post-event return → corpus → train a transformer → predict

The graph is what makes the features deep (relationships, not just numbers) — and depth is exactly what a price-only model can't see. That's the moat, and the road from here.

BUILT on the MARS entity graph via the isolated graph surface (graph_resolve → graph_neighbors over insider_of / studied_at / worked_at / board_member_of). Point-in-time: edges carry as_of; this dossier renders the "Live" dimensions — the "Roadmap" dimensions (partnerships / products / customers / competitors) land as the commercial + event edges ship. Illustrative graph-native profile from public filings — not investment advice. Internal — not for external distribution.