A design-partner conversation

Private, provable AI for VC financings and legal work.

Private and provable. Two working prototypes — one built for a real VC fund, one for legal work — running fully offline today. Not a sovereign chat box: an engine that computes, proves its work, and is backed by a real company that maintains it — not unwarranted open source. Built, and yours to shape.

Request a demo on your matters

AI proposes. Lawyers decide.

The proof — two working prototypes

Two working prototypes — one that computes instead of guessing, one built to answer only from real authority.

Both run fully offline — real and demoable, not finished products.

A VC fund-ops engine

PROTOTYPE

Built for a real VC fund: deterministic valuations, waterfalls, and a tamper-evident ledger an auditor can independently rebuild. Proof the engine — and the audit chain — work.

GoDo Counsel

PROTOTYPE

Ask a legal question, get answers cited to your local case-law corpus with a citation-graph signal. It answers only from real authority — limited to what you load (today a focused corpus, expanding toward full US public law) — and is built to refuse to invent a case, citation, or holding, flagging a gap rather than guessing.

A natural pairing: the funds your firm advises are exactly who the fund-ops engine serves.

Why a free AI workspace isn't this

A self-hostable chat box is free now. The product is the part it can't be.

On confidential, audited work you need three things a chat box structurally can't be.

Computes

A real waterfall, a real conflicts traversal — instead of hallucinating one.

Provable

A record an auditor can independently rebuild, not "the AI said so."

Backed

A maintained product with a real, named company behind it — not "WITHOUT ANY WARRANTY" open source where the risk is all yours.

Sovereignty is where it runs, not the product.

Built for how a careful lawyer distrusts AI

Private and provable — the two things cloud AI fails on confidential work.

Privilege preserved

In U.S. v. Heppner (S.D.N.Y. 2026) a court held that sharing confidential information with a public AI tool waived privilege. Sovereign deployment means the data never leaves your building — so there's no third-party disclosure of the kind at issue in Heppner.

No fabricated authority

Grounded, cited, and built to refuse to invent a citation — the failure that's drawn court sanctions against lawyers using public chatbots.

Supervisable (ABA Op. 512)

Prompt-injection defense, fail-closed default-deny, everything logged — AI you can supervise and stand behind. AI proposes; lawyers decide.

Sources: U.S. v. Heppner, S.D.N.Y. (2026) — analyzed by Fenwick, Perkins Coie, Akin Gump, K&L Gates; Harvard Law Review. ABA Formal Op. 512 (2024). Courts are split and the area is evolving.

Where we start — and where it goes

Start by automating the VC Financings Survey you build by hand.

Phase 1 · the survey

Automate the cross-deal term extraction

The work you do by hand each quarter — your team checks that the citations are real and inspects the audit chain.

Phase 2 · conflicts + supervision

Relationship-graph conflicts

A real graph traversal across your matter history. Partner approvals from a phone-installable companion.

Phase 3 · cross-deal intelligence

An always-on survey

Because it runs on your data, it compounds into cross-deal intelligence that gets richer the more your firm uses it.

Runs cloud or sovereign from one codebase — your AI/infra team runs it on your hardware; we bring the engine and keep the legal domain current.

What's it worth — and the ask

Priced to the value it creates — not by the seat.

Value levers

Time saved on the Survey and conflicts · a single privilege incident avoided · business won and kept (DDQ pass, client trust) · per-seat cloud AI you stop renewing.

The ask

A demo on your own matters — your team checks that the citations are real and inspects the audit chain. Then we run your first Survey and price off the value it proved.

How it's priced

Flat and firm-owned — no per-seat licenses, no usage uplift. We size it to the value with you, and you'd run it on hardware you own.

Appendix · for your InfoSec & GC team

Sovereign by architecture: your data stays yours.

Where it lives: in the sovereign deployment, the model, index, and matter data run on your hardware/VPC — privileged text is processed in memory on your hardware, never sent to a third-party service.
No third-party AI processor to vet: nothing routes to an external AI service, so there's no vendor sub-processor for your team to assess — you are the data controller.
Access: your own SSO/SAML or one-time-passcode; in the sovereign tier GoDo has no access to your data or your matters.
No training: your data never trains our models — or anyone else's.
Hardening already in the prototype: prompt-injection defense, fail-closed default-deny gate, loopback-only egress, error redaction, encryption at rest, tamper-evident audit ledger.
Privilege: in U.S. v. Heppner (2026) a court held that sharing confidential data with a public AI tool waived privilege — sovereign deployment means there's no third party to disclose to.

Deployment	Data location	Privilege-sovereign
Managed cloud (multi-tenant)	GoDo cloud	No
Single-tenant cloud (your VPC)	Your cloud	Boundary you control
Self-hosted (your LAN / box)	Your hardware	Yes — flagship