AI Governance
Before Consequence
ENTRUST AI builds the controls that decide what an AI system may release, say or do before it becomes an institution’s commitment: deterministic gates, governed agents, and signed evidence that the control fired.
Five Products.
One Thesis.
Four are live and running in production. LUCID is on its way to the Chrome Web Store. Each governs a boundary: what AI may release, what an agent may do, what a professional sends and accepts, and what crosses from one session to the next.
The governance engine for agent sessions. Holds the reins of every conversation — enforcing declared objectives, detecting drift, and correcting before the carriage loses direction.
Explore CGM →
Certified Writing · Adversarial Examination · Signed Receipts
Open APEX →
AI Session Continuity Engine · Chrome Extension · Multi-Platform
Get Fractal Memory →
AI Governance Enforcement · RELEASE / REJECT · Cryptographic Audit
Launch The Pilcrow™ →Pressure-Tests the Prompt · Reviews the Answer · Against Your Professional Standard
Meet LUCID · Coming to Chrome →Govern Before
Consequence
The products rest on one argument: an institution must govern what AI may commit it to at the boundary, before the consequence, not after it.
The Bounding Gap
Why Institutions Must Govern Before AI Becomes Consequence
Most AI governance watches the model. The book argues that the object to govern is the crossing: the moment generated language asks to become institutional reality. It names the distance between what a model can generate and what an institution may authorize, and shows why observation, monitoring and review after the fact cannot substitute for executable refusal at that boundary.
Get the book on Amazon →Research
Five papers on arXiv and Zenodo, from the phase structure of attention to the adequacy conditions for representing felt orientation, and a preregistered study on the Open Science Framework of whether an AI system’s rule-following survives a session reset, run across four AI models.
See the full record on ORCID →Writing
54 articles on AI governance on the Substack The Bounding Gap, among them the series The Institution Before the Machine.
Read on Substack →Governance You Can Deploy
ENTRUST AI builds the boundary between what AI can generate and what your institution may authorize, and puts it into production: as a gate in your agent stack, as a product, or as the architecture before either.
Everything a model can write, decide or do. Fluent, fast, unbounded, and not yet anyone’s responsibility.
Only what your institution has authorized, released with a record of who allowed it, under which rule, and why.
Govern Your Agents
A release gate in front of every action an AI agent takes in your name, running in your stack.
- Session governance for agents, from observe to enforce
- A deterministic output gate: release or reject, never maybe
- Cryptographic seals and signed receipts anyone can verify
- Independent judges across Anthropic, OpenAI and xAI
Build Governed Products
Five products built end to end, from sign-in and payment to the model layer. The same hands build yours.
- Multi-model orchestration with cascading fallback
- Subscriptions, payments and enterprise sign-in
- Chrome extensions and apps inside ChatGPT
- Telemetry that reports when a dependency breaks
Govern by Design
The architecture before the first agent ships: which boundaries exist, what may cross each, and who refuses.
- A boundary map of your AI deployment
- A registry of every path to consequence
- Executable refusal, not review after the fact
- Evidence your auditors and regulators can read
Follow ENTRUST AI on LinkedIn
Follow on LinkedIn