We built the engine for doing to litigation what quant algorithms did to stock trading.
At the small end of the spectrum, valid commercial debt and healthcare receivables sit on ledgers because the cost to liquidate them exceeds their worth. At the large end, even six- and seven-figure disputes carry bloated cost structures that erode recovery. The capital is trapped across the entire range.
We acquire these distressed claims at 5–15¢ on the dollar. Our proprietary engine drives the marginal cost of prosecution to near zero, capturing a structural spread that traditional firms cannot reach.
Returns are uncorrelated to equities, rates, and real estate. Whether the economy is expanding or contracting, insurers still deny claims and businesses still breach contracts. And every claim gets its fair day in court.
This is a high-yield, uncorrelated asset class.
I. Verified debt;
II. Efficient prosecution;
III. Low-cost execution.
Executes specialized legal prosecution to maximize win rate. 95% cost compression. Local counsel reviews and signs every filing.
On the Stanford 100-query legal-AI benchmark, Veracity-Engine returned 100 correct answers and zero hallucinations. By comparison: Lexis+ AI hallucinates 17%, Westlaw Precision AI 33%, GPT-4 43%, and Thomson Reuters' Ask Practical Law AI 17%. Practical Law's number is the denominator trick — it refuses 62 of 100 legal questions and hallucinates roughly one in six of the rest.
What that looks like in practice: Westlaw fabricated a jurisdictional rule for FRBP 4007 that Kontrick v. Ryan, 540 U.S. 443 (2004), directly rebuts. Lexis+ AI told users that Casey's undue-burden test still governs abortion regulation — three years after Dobbs overturned it. Thomson Reuters fabricated a Justice Ginsburg dissent in Obergefell with the fabrication about copyright (wrong topic, wrong opinion, wrong author). vLex's citator marked a unanimous Marshall SCOTUS decision as having received "negative treatment" from a federal district court — structurally impossible.
Inference Relay is our proprietary compute routing infrastructure (patent pending, commercially licensed at inference-relay.com). It reduces token costs by approximately 50× by routing heavy execution through native CLI and local processing rather than metered API billing. This is a structural moat that no competitor can replicate without building equivalent infrastructure from scratch.
AI-powered litigation at scale would be economically prohibitive using standard API pricing. High-context legal document processing (OCR extraction, multi-step drafting, citation verification) costs hundreds of dollars per day in testing alone and would scale to tens of thousands monthly in production. These costs would destroy the fund's unit economics before a single claim is filed.
United States v. Heppner drew the line.
On February 17, 2026, Judge Rakoff (S.D.N.Y.) held in Heppner that communications with an AI platform lose attorney-client privilege the moment the platform's terms permit the operator to access user data. Capability of access, not actual disclosure, defeats confidentiality.
Kovel preserves one path: AI as counsel's privileged agent, but only when the operator structurally cannot access client communications. The test a court will apply is the platform's own terms.
The same week, Judge Patti (E.D. Mich.) reached a different result in Warner v. Gilbarco — but only on the work-product doctrine, which waives only on disclosure to an adversary. Heppner's privilege analysis stands. Prudent firms plan for the more demanding standard.
“Knowledge Source and Web Browsing subprocessors are not HIPAA compliant.”— §4.4
“Customer data may not process in Your selected data processing region.”— §4.4 / §4.5
“[User] authoriz[es] us to access and share Your Data with the third-party provider.”— §2.1
“the Syncly DMS service may be hosted in a single or multi-tenant environment in our discretion.”— §3.3
Lexis+ AI, Westlaw Precision AI, and most legal AI products (vLex, Harvey, CoCounsel, etc.) run on third-party foundation models (OpenAI's GPT family, Anthropic Claude, others). The customer contract is with the legal AI vendor, not with the foundation-model operator whose servers actually run the inference. Zero-data-retention commitments in the vendor contract do not reach the upstream provider. Under Heppner's capability analysis, the customer has no privity with the entity that holds the access capability.
“[T]he written privacy policy to which users of Claude consent provides that Anthropic … reserves the right to disclose such data to a host of 'third parties,' including 'governmental regulatory authorities.'”— United States v. Heppner, No. 25 Cr. 503 (JSR), slip op. at 6 (S.D.N.Y. Feb. 17, 2026)
Sovereign Shield is the architecture Heppner contemplates. Unlike Harvey's subprocessor chain, CoCounsel's authorized third-party sharing, or the foundation-model gap in Lexis and Westlaw, Sovereign Shield gives the platform operator no path to access client communications. The privilege guarantee is enforced by hardware, not by promise.
Sovereign Shield gives the firm a choice: inference in the firm's own browser using its own API key (BYOK), or inside hardware-isolated Trusted Execution Environments managed for the firm (Enterprise / Professional). All three tiers share one structural guarantee: the platform operator cannot access client communications.
Operator access is mathematically impossible, not contractually restricted. Subprocessors: zero. Retention: hardware-enforced zero, with cryptographic attestation. Privilege survives by construction.
Comparison current as of May 2026. Vendor terms can and do update. The mechanism we use here — verbatim citation to specific clause numbers, dated — is the same mechanism a court will use in a privilege dispute.
Where the engine deploys.
Every claim comes with a signed Bill of Lading and Rate Confirmation. Immaculate paperwork. Defendants are typically operating businesses that settle quickly. Fee recovery may be available under the Carmack Amendment.
Insurers use algorithms to mass-deny commercial claims for administrative reasons. Providers sell denied portfolios at steep discounts. ERISA and state insurance codes provide the cause of action. Bulk settlement is the norm.
Unpaid invoices governed by aggressive, one-sided vendor terms. Mandatory fee-shifting clauses, late penalties, and explicit choice-of-law provisions are standard.
Subcontractors with timely mechanic's liens hold debt secured by real estate. The property owner or title insurance forces settlement to clear title. Collection risk is structurally mitigated by the real estate securing the debt, provided statutory notice requirements are met.
Insurance companies hold thousands of low-value subrogation claims ($5K–$15K) they never pursue. We acquire the recovery right and deploy Veracity-Engine at a fraction of the traditional cost.
Capital is spread across hundreds or thousands of claims, not concentrated. The big cases are upside. The small cases are the fuel.
Sole architect of Veracity-Engine, Plaintiff Zero, and inference-relay. Co-founder and lead scientist of Blecher Group; co-designer of BrashZero. Special Counsel for CFIUS, ITAR, and dual-use export controls.
M&A and Securities at Kirkland & Ellis LLP (NYC). Securities Law at Stikeman Elliott LLP (Toronto). JD, Osgoode Hall — 1st place in US Securities Regulation and Business Associations. Founder of Blecher LLC, Flying Gun, and Blecher Precision.
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Judge Rakoff's February 2026 S.D.N.Y. ruling on whether AI-assisted legal work is privileged. Why the platform's underlying architecture, not its privacy policy, decides whether attorney-client privilege survives.
Veracity-Engine on the Stanford Legal AI hallucination dataset. 100 queries, 0 hallucinations, 0 refusals. Leading competitors hallucinate 17–33%. The structural reasons retrieval-only legal AI keeps failing and why verification architecture carries the load.
For CTOs, CISOs, and security auditors. How the library is an enterprise procurement shortcut, bringing Shadow AI into compliance by routing through already-vetted organizational subscriptions. Three deployment architectures, none of which creates a new data processor.
How a mocked hypothetical in the birthright citizenship case quietly maps the constitutional basis for global prosecutorial reach. Citizenship confers allegiance; allegiance determines prosecutorial reach; defining who owes allegiance defines who can be prosecuted.
The disruption thesis was that AI would let small builders out-compete incumbents. Project Glasswing shows the opposite: architecture decides winners. Frontier capability plus centralized billing plus partner-gated distribution equals incumbents winning by default.