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Fiber AI (Y Combinator S23) — Firm-to-firm talent flow graph: for any company, the origin-destination edges of employee movement within a chosen date window — joiners showing which competitors, universities and regions it recruits from, and leavers showing which companies and startups absorb its former staff. Output is a bucketed flow matrix with counts and shares per counterparty employer, enriched with that counterparty's funding stage, total funding and valuation, keyed by LinkedIn company identifier, slug, numeric organization ID or domain. Backed by a professional-profile corpus queryabl…
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Firm-to-firm talent flow graph: for any company, the origin-destination edges of employee movement within a chosen date window — joiners showing which competitors, universities and regions it recruits from, and leavers showing which companies and startups absorb its former staff. Output is a bucketed flow matrix with counts and shares per counterparty employer, enriched with that counterparty's funding stage, total funding and valuation, keyed by LinkedIn company identifier, slug, numeric organization ID or domain. Backed by a professional-profile corpus queryable up to 10,000 profiles per request.
From 10 yearsCoverage Information Technology · FinancialsAsset class Equities
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Fiber AI (Y Combinator S23) — Firm-to-firm talent flow graph: for any company, the origin-destination edges of employee movement within a chosen date window — joiners showing which competitors, universities and regions it recruits from, and leavers showing which companies and startups absorb its former staff. Output is a bucketed flow matrix with counts and shares per counterparty employer, enriched with that counterparty's funding stage, total funding and valuation, keyed by LinkedIn company identifier, slug, numeric organization ID or domain. Backed by a professional-profile corpus queryabl…
Fiber offers (Alternative, Reference, Sentiment) — est. hundreds of millions of professional profile records underpinning the flow computation (the endpoint processes up to 10,000 profiles per company request, implying a corpus large enough to resolve full employer rosters at scale); total corpus size is not published.
Deriving competitor sets empirically from labour interchange rather than declared industry classification — a network adjacency that no filings-based or postings-based source produces; early detection of a rival's team build-out in a new capability area; diligence on key-person concentration and post-acquisition attrition; private-credit and venture underwriting on whether a startup is winning or losing its talent war; predicting the emergence of spin-out competitors from alumni flows; and training graph models of labour-market diffusion.
The data is with 10 years of history.
Coverage spans US; Application Software, Financial Exchanges & Data; alternative, reference, sentiment; equities.
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