Factify

Factify is truth infrastructure for AI agents.

Enterprises are putting agents into workflows that touch regulated, high-stakes decisions: underwriting, claims, lending, compliance. The bottleneck is not model quality. It is that agents are reasoning over scattered, conflicting, and stale source material - policies in binders, rules in spreadsheets, exceptions in someones inbox. A smarter model just gets better at explaining a decision made on the wrong input.

Factify reconciles that scattered knowledge into certified, versioned policy objects, compiles approved policy into deterministic enforcement logic, and ties every agent action back to the source, version, and approval that authorized it. Agents do not guess which rule is active. They execute against what is actually in force, and every decision comes with a full audit trail.

Factify runs in three stages:

Reconcile - Ingest policies, files, databases, SaaS records, emails, and internal decisions. Turn them into versioned policy objects with owners, sources, approvals, and effective dates.

Enforce - Compile approved policy into logic that runs at runtime. Every agent action is checked against the active version before it can update a record, trigger a workflow, or return a decision.

Prove - Every allowed or blocked action is logged with the policy version, source evidence, approval trail, owner, timestamp, and reason.

This is not RAG and it is not a guardrail. RAG retrieves documents; it does not decide which policy is active or which source wins when records conflict. Guardrails sit around the model; they do not enforce policy before it reaches the business system. Factify sits underneath both, as the layer that governs what is actually true and enforces it before an agent can act on it.