AI-Native VC & PE Funds.
14 case files on what it takes to encode a specialist fund's methodology as operational substrate — audit-chained, query-replayable, defensible to LPs and regulators, durable across model cycles and partner transitions. One file per fund. Numbered. Tagged. Cross-referenced.
BUILDING NATIVE INTELLIGENCE, CASE BY CASE.
Each case is a structured analytical reconstruction from public filings, regulatory documents, trade-press statements, and methodology inference — not a customer engagement. Queryable Fund is pre-revenue. The MVP is operational. No live customer is represented anywhere in this series.
Cases follow a shared spine — frame · spine · per-portco evidence regime · substrate · Phase 0 proposition — so a reader moving between cases compares the same surfaces. The tether-pair discipline is preserved throughout: every abstract claim ships with the mechanism, file path, or measurement it rests on. Design-partner slot open.
Grouped by segment.
AI OS for the Urban Stack at €500M · climate adaptation across the built environment.
Article 9 carbon-math at €204M · LCA chains hardened as auditable substrate.
LCA methodology · evidence beneath the model · Article 9 climate-tech at scale.
Healthtech specialist · pre-clinical evidence asymmetry · dual-pathway diligence as substrate.
Healthtech first-time fund · evidence-mode discipline encoded before first close.
Youth mental health · evidence-mode tagging across pre-clinical signal and lived experience.
Youth flourishing · outcomes-tied diligence · methodology overlay encoded at fund formation.
Published impact-carry formula meets non-linear evidence-chain growth — and an SFDR 2.0 replay.
€92M Article 8/9 NL coalition · uniform-reporting commitment · nine non-identical LP reading frames before SFDR 2.0.
Analytical reconstruction · Article 9 fund under impact-tied carry economics · methodology and audit-chain pattern. No live engagement. Design-partner slot open.
Consumer-impact thesis · conviction-led diligence encoded as substrate before scale.
Article 9 consumer impact at £80M · per-portco evidence reconstructability under SFDR 2.0.
Multi-fund-family · cross-fund federation under one substrate · k ≥ 8 anonymity floor.
Three entry paths depending on who you are.
How the evidence reads under nine frames.
Multi-LP reading frames, cross-fund federation, and an Article 9 deployment-stage replay.
What survives second close.
A first-close coalition, a first-time fund pre-close, and a published-formula fund under SFDR 2.0 pressure.
What encoding would look like.
An anonymized Article 9 reconstruction, a carbon-math substrate, and an LCA-methodology substrate.
Each case is constructed from on-record material — trade-press statements, regulatory filings, fund websites, public LP communications — paired with the operational primitives Fund AI OS builds against. The interpretive layer is named as such; the fund's own commitments are quoted verbatim.
The substrate primitives referenced across the series — audit chain, methodology overlay, multi-LP reading frames, impact-carry formula, cross-fund federation, Phase 0 diagnostic — are documented in full on /substrate and /methodology.
Evidence reliability target: κ ≥ 0.85 (Cohen's κ weighted variant). Tether-pair discipline: every abstract claim ships with the mechanism it rests on.