Every AI decision, verified.
10-layer Decision Trust OS for production AI systems.
A production-grade trust layer that wraps any AI decision with uncertainty quantification, attribution, formal verification, and constitutional guardrails — ready for EU AI Act and Korean AI Basic Act compliance.
AI systems produce decisions. But decisions without trust are liabilities. These are the five gaps that separate AI outputs from defensible decisions.
We do not know what the AI looked at to make its judgment.
We heard the explanation. But is it actually correct?
Assume it is correct. If it is wrong, how dangerous is it?
It cannot be defended in front of the board.
It seems fine for now. But will it still be fine next time?
Each layer adds a distinct trust dimension. Together, they form a complete verification stack from policy enforcement to mechanistic interpretability.
Policy-as-Code enforcement, 3-tier identity management, and action approval gates. The foundation that governs every trust operation.
Embedding-based drift monitoring that detects when input distributions shift away from training baselines, before predictions degrade.
Distribution-free statistical guarantees with 3 prediction engines. Know the uncertainty of every output without distributional assumptions.
Feature contribution analysis with dual-seed stability. Understand exactly which inputs drove each decision and verify attribution consistency.
Value-based guardrails beyond formal rules. Fairness, transparency, and human oversight verification through constitutional AI principles.
Trace, Eval, Improve — a continuous production loop with calibration monitoring that keeps trust scores accurate over time.
Trust Intelligence maps directly to regulatory requirements — not as an afterthought, but as a core design constraint.
Transparency (Art.13), human oversight (Art.14), and accuracy/robustness (Art.15) requirements mapped to trust layers with automated evidence collection.
Reliability assessment (§31), transparency obligations (§32), and impact assessment (§23) compliance with continuous monitoring and audit trails.
Native integration wrappers for the leading agent frameworks. Add trust to any agent pipeline in minutes.
Model Context Protocol server for tool-based trust verification.
Trust pipeline as OpenAI agent tools and guardrails.
Native Anthropic agent integration with trust verification.
Trust nodes and edges for LangGraph agent workflows.
Production-ready trust layer for AI systems. Open source.