The Verification Layer

A structured pipeline that sits after LLM generation and before response delivery. It doesn't replace RAG or guardrails — it completes the stack.

Where It Fits

The enterprise AI stack is evolving in layers. Verification is the missing one.

RAG: Retrieve context
LLM: Generate response
Guardrails: Safety check
Verification: Domain compliance
Verified Response (with audit trail)

Core Capabilities

Claim-Level Extraction

An LLM response contains multiple verifiable claims. "We'll process your full refund of the $35 fee immediately" contains at least three: refund will happen, full amount, immediately. Each is extracted and verified independently.

A response can be 90% correct and still contain a single claim that violates a critical policy rule. Whole-response scoring hides this. Claim-level extraction surfaces it.

Domain Rules as Configuration

Banking has TILA, FCRA, ECOA. Insurance has coverage terms and state-mandated timelines. These rules are structured, versionable, testable configuration — authored by domain experts, not engineers.

When rules change — new regulation, updated policy — the domain expert updates configuration. No code deployment. No engineering sprint.

Auditable Decision Traces

Every verification produces a complete record: what claims were made, which rules were checked, what the score was, and whether the claim passed, was flagged, or rejected.

This is the difference between monitoring (dashboards, aggregate metrics) and auditing (per-decision evidence trails that hold up under regulatory scrutiny).

Technical Differentiators

Mathematical, not probabilistic

Defined distance metrics with thresholds, not probability estimates from another LLM.

Per-claim, not per-response

A response with four correct claims and one critical violation is not "80% compliant."

Reproducible

Same input, same output. Deterministic verification, not stochastic evaluation.

Audit-native

Decision records are first-class outputs, not logging afterthoughts.

Domain-agnostic engine

The engine verifies. Domain knowledge lives in configuration files.

API-first

Integrates into existing AI stacks as a verification layer.

Ready to verify your AI outputs?

Talk to us about compliance verification for your regulated AI deployment.