For the buyer
The One-Pager.
Print it. Hand it across the table.
DISCINTEL is behavioral trust infrastructure for AI that acts.
It sits in the execution path of an AI persona and evaluates every consequential action before it takes effect. It returns one of three verdicts and records each one, approvals and refusals alike, in a hash-chained evidence layer designed for independent verification.
It is the difference between "our AI is governed" and "here is the governed action, the verdict, the owner, and the evidence — and here is how you check it without us."
What it governs.
- Who acts. Every AI persona carries an identity, an accountable human owner, and a Behavioral Constitution: permitted tools, permitted jurisdictions, operational status. The constitution is hashed; any change to the bounds is detectable.
- What it may do. Authority is bounded by construction. Anything not granted is not permitted.
- Whether it proceeds. In-band evaluation before effect. HOLD is bounded — it expires, and an unresolved HOLD resolves to BLOCK. If the governor cannot evaluate, the verdict is BLOCK.
- What the record shows. Every verdict is a hash-chained evidence event. Refusals are first-class.
The Governed Action Proof.
The entry engagement is a proof, not a platform license. One consequential action class in your process is placed under in-band governance, and the resulting evidence is independently re-observed.
- Persona and constitution. One AI persona, one accountable owner, one ratified authority envelope.
- Governed path. One action class from your consequential-action floor — claims settlement, payment release, access grant, PII export — routed through the decision point.
- Live verdicts. ALLOW / HOLD / BLOCK on real or replayed traffic.
- Evidence chain. A TrustDNA record covering the engagement, with annotated chain roots.
- Independent readback. Your auditor confirms the evidence matches the effect, without DISCINTEL in the room.
The proof establishes that the three claims below hold for that action class in your environment. It makes no claim about action classes it did not govern.
Fixed scope. Fixed fee. Document-based discovery first; no system access until you say so.
Three things we will defend. Five we won't.
Defended
Speed asymmetry. Accountability preserved. Verifiability.
Not claimed
Improved model performance. Human replacement or machine autonomy. Attainment of any conformance level before independent certification. Guaranteed regulatory compliance. Regulator endorsement.
No model rewrite. No infrastructure replacement.
- Governance is model-agnostic. Your model, your provider, your MLOps stack stay where they are.
- Integration is at the tool-call boundary. Read-only by default until a governed path is ratified.
- You do not share weights, training data, or proprietary IP with us.
Built for the exam clock.
U.S. regulators are moving first: the NAIC AI Systems Evaluation Tool, state model bulletins, and federal supervisory guidance are converging on the same question — can you show, not tell, that your AI is controlled? DISCINTEL is designed to produce the on-demand Regulatory Evidence Package that answers it, mapped to the jurisdiction asking.