$15 million. That is what Tetrix, an AI-native investment intelligence platform, just raised in Series A funding co-led by White Star Capital and Innovation Endeavors. The round values the New York-based company's technology for an older, messier problem: private markets run on PDFs.
It collapses 45-day analyst workflows into a single day, managing over $100 billion in assets across endowments, pension funds, and sovereign wealth funds.
Its Series A signals that institutional capital is ready to pay for AI-native infrastructure that turns unstructured fund data into structured, usable intelligence.
The PDF graveyard of private markets
Private markets have grown at double-digit rates for a decade. Limited partners (pension funds, endowments, family offices) now allocate trillions to alternatives. But the tools they use to track those investments have barely changed. Fund data arrives as PDFs, spreadsheets, and email attachments. Custodians run on portals built before cloud computing. The result is a fragmented data layer where the same information gets re-keyed into separate systems, reconciled manually, and updated quarterly at best.
Tetrix CEO Olivier Babin calls it a "100 million PDF" problem. He and co-founder Naunidh Singh Bhalla met at Stanford Graduate School of Business, both coming from private-market investing and fintech engineering backgrounds. They saw the same gap: public markets have Bloomberg terminals, real-time pricing, and standardized data feeds. Private markets have a PDF graveyard.
"Alternative markets are an over $20 trillion asset class running on 100 million PDFs," Babin said. "Our clients' data is their most valuable asset to generate returns and manage risk. Yet for LPs in alternative markets, the unstructured data, PDF graveyard, fragmented solutions, and manual workflows mean their data is rarely treated as such."
Its platform automates data collection from fund reports, extracts key financial and qualitative information using AI, and delivers structured analytics. Since its commercial launch in September 2024, the platform has onboarded dozens of institutional investors across the US, Canada, Europe, and Southeast Asia. Over $100 billion in assets is managed through the system.
Ben Choi, Managing Partner at Next Legacy, described the before-and-after directly. "Before Tetrix, manager diligence was clunky and manual, spread across too many systems. The customizable analytics supercharge our ability to gain insights from our portfolio of over 30,000 companies and hundreds of funds."
Global private-market AUM surpassed $14 trillion in 2025. Yet the typical LP allocator spends 40–60% of their time on data collection and verification rather than analysis. The company inverts that ratio: AI extraction handles the plumbing, humans focus on the judgment calls.
From 45 days to one
The pitch is specific enough to verify. It claims its platform collapses a 45-day fund-analysis workflow into a single day. The company says it saves customers thousands of manual hours per investment cycle. Those are the kinds of metrics that move institutional procurement cycles, where replacing a manual process requires demonstrable time savings across a multi-year subscription.
The investors behind the round reinforce the thesis. White Star Capital general partner Christophe Bourque framed it in infrastructure terms: "Private markets manage trillions in capital but still rely on data infrastructure built for the 1990s. Tetrix is filling a massive gap by building a foundational, AI-native data and orchestration layer for the industry."
Innovation Endeavors partner Harpinder Singh doubled down on the workflow angle: "Tetrix is already collapsing 45-day analyst workflows into a single day and saving customers thousands of manual hours per investment — that is a category-defining step change, not an incremental productivity gain."
Recent regulatory shifts are accelerating the trend. The Department of Labor's 2026 rule changes expand access to private-market investments for a broader set of capital allocators. More participants means more data, more reports, more PDFs — which in turn creates more demand for the infrastructure layer it is building.
The competitive picture
Tetrix is not alone in targeting this gap. Horizontal AI document-extraction tools like Glean and Hebbia can handle unstructured data generally. Incumbent data providers like PitchBook and Preqin offer private-market analytics but rely on analyst-researched databases rather than direct fund-report ingestion. What distinguishes it is the vertical focus: the platform ingests the actual fund documents LPs already receive, extracts portfolio-level metrics directly, and maps them to each allocator's internal taxonomy without manual tagging.
The company says clients include endowments, foundations, pension funds, sovereign wealth funds, fund-of-funds, and single-family offices. That customer distribution, across both institutional and private capital, suggests the problem is structural rather than segment-specific. Every type of LP with private-market exposure generates the same reconciliation work.
What happens to private-market data infrastructure a year from now?
Probability: 65% — the regulatory tailwind and competitive pressure from early adopters will compress procurement cycles. The laggards will be smaller allocators with less leverage over GPs to standardize reporting formats.
✅ Arguments for
Confirmation criteria: Two or more competitors raise comparable Series B rounds within 12 months; a top-10 US pension fund issues an RFP specifically for AI-native LP reporting software.
❌ Arguments against
Disconfirmation criteria: No major LP mandates AI extraction in the next two reporting cycles; incumbent data vendors add equivalent functionality before the company scales its customer base.
Development scenarios
🟢 Optimistic scenario (25%)
Implications: The platform becomes the system of record for a $20 trillion asset class. Series B rounds at 5–10x current valuation within 18 months.
🟡 Base-case scenario (55%)
Implications: Sustainable standalone business with a clear path to Series B. The company becomes an acquisition target for Bloomberg, MSCI, or a major custodian bank looking to enter private-market data infrastructure.
🔴 Pessimistic scenario (20%)
Implications: It pivots to a narrower segment or sells at a discount. The thesis that "AI for private-market data" is a standalone category fails to materialize.
Adoption by at least two of the top 20 US pension funds within 12 months.
Whether PitchBook, Preqin, or Bloomberg launches a competing AI extraction feature tied to existing subscriptions.
GP-side resistance: if large fund managers begin standardizing data delivery formats, it accelerates Tetrix's model; if they actively resist, it entrenches the PDF-graveyard status quo.
DOL enforcement of the 2026 fiduciary rule expansions — more LP participants means more demand for data infrastructure.
The company was founded in 2023 by Olivier Babin and Naunidh Singh Bhalla, who met at Stanford GSB. Their backgrounds span private-market investing at major allocators and fintech engineering. The Series A brings total funding to $20 million, following a $5 million seed round led by Innovation Endeavors.
