Private capital is the most opaque corner of global finance. $13 trillion in assets. Almost none of it structured, searchable, or auditable in real time.
Aumni cracked something real. If you extract data from the actual legal documents - SPAs, SAFEs, side letters, cap tables - you can bring public-market clarity to a private market that has never had it.
That idea was good enough that JPMorgan bought the company.
On March 31, they're shutting it down.
Let that sit for a second. The largest bank in the world acquired a company built on a genuinely important thesis - that private capital deserves structured, transparent, auditable data - and concluded it wasn't worth keeping alive.
The Mission Didn't Fail. The Business Model Did.
You cannot staff a legal data extraction operation with human reviewers inside a bank's cost structure and survive. The unit economics of manually reading legal documents, tagging provisions, and building structured databases don't work when every incremental document requires incremental human labor.
This isn't a JPMorgan problem. It's a fundamental architecture problem. Every company that has tried to bring transparency to private markets through human-powered extraction has hit the same wall. The cost of accuracy scales linearly with volume, and at bank-grade overhead rates, the math breaks before the product reaches its potential.
What Aumni Built - and What's Being Lost
Structured data extracted from thousands of VC legal documents. Audit-ready portfolio intelligence built over years of painstaking manual review. The only scaled attempt to make private capital transparent at the document level.
That knowledge graph took years and millions of dollars to assemble. On March 31, it goes dark.
Why the Economics Are Different Now
The extraction problem Aumni solved with human reviewers is now solvable with purpose-built AI agents. Not general-purpose chatbots pointed at PDFs. Specialized extraction agents - each trained on a single data field, validated across multiple LLMs - that can read a legal document and extract structured data with 97% accuracy.
The difference isn't just speed. It's cost structure. AI-native extraction means the marginal cost of processing the next document approaches zero. That's what changes the economics from "interesting but unsustainable" to "inevitable infrastructure."
GoodStream is built on this thesis. We deploy 171 specialized extraction agents that turn every email, PDF, and spreadsheet into a living, audit-ready knowledge graph - automatically, continuously, at a price point that works for funds of every size.
The transparency Aumni promised is now deliverable at scale. Not because the mission changed, but because the technology finally caught up to the ambition.
Private capital deserves the same clarity as public markets. That belief didn't die with Aumni's shutdown. It just found a business model that works.
If you're an Aumni customer looking for a path forward, book a demo and we'll show you how GoodStream picks up where they left off.
