Enterprise legal teams and their outside counsel already spend $30M+ per company. Only 2% of that spend is software — and 95% of AI deployments never reach production because continuous ownership and verification are missing.
Ciph Lab is building the continuous ownership layer that makes high-stakes AI legal work production-ready. Named Governance and Verification roles are attached to the live document and checked before work continues. We start where spend and risk collide hardest: IP.
A policy is updated by one team but never reaches the group still working from the old version. A client file moves from associate to paralegal to partner — and no single record shows who is accountable for what is still outstanding. The same gap now exists when AI tools generate or edit those documents.
The result is rarely a dramatic failure. It is constant and expensive: version drift, missed sign-offs, unclear ownership, and the quiet erosion of accountability across both firm policies and client matters.
Every document — policy or client matter — carries its own accountability requirements. Responsible, Accountable, Consulted, Informed, Governance, and Verification become live conditions, not fields filled in after the fact. Empty means blocked. Named means cleared.
GRACI™ moves accountability out of static PDFs and into live enforcement. Every dimension — Responsible, Accountable, Consulted, Informed, Governance, and Verification — becomes a required condition the system checks before work proceeds. This model has been validated as a working prototype and is now being built into the continuous ownership platform, starting with IP. See how GRACI™ works →
Every document has a named accountable party — not a shared inbox or an assumption. That's the chain you can point to when a client or auditor asks who signed off.
See what's been signed off and what's still waiting — without digging through inboxes to piece it together.
Know who touched the file, and when — a record you can produce, not institutional memory you have to reconstruct.
Every AI-assisted step has a named owner who governs which tools are used, and a named owner who verifies the output before it's treated as final — a direct answer when a client asks how AI use is being managed.
Consistently reviewed and traceable, so the version everyone is working from is always the right one — with a clear record of who approved what, and when.
Know who is responsible for what, at every stage of a matter — without relying on institutional memory or a scattered email trail.
This is not about replacing legal judgment with AI. It is about making sure the structure around that judgment — who is responsible, what has been checked, and what is still open — is enforced by the system instead of depending on someone remembering to follow up.
That matters more now: clients increasingly expect continuous ownership and verification of AI use, and the professional responsibility for getting it right hasn't gone anywhere.
The platform is currently in development. We're seeking a small number of design partners — IP groups and enterprise Legal Ops teams — to help shape the product before it is fully built. Book a conversation to explore fit.