Platform

How EvidentAI works under the hood.

EvidentAI is designed to sit alongside existing GRC platforms — OneTrust, Archer, ServiceNow, MetricStream — and feed them continuous, normalized evidence through a Canonical Control Schema.

01 · The Governance Loop

Every inference flows through five stages.

Capture, policy evaluation, enforcement decision, evidence emission, and GRC integration — all five run in-path, with sub-second latency on the enforcement decision.

Stage 3 outcomes

Allow
Route
Block
Require Sign-off

Every inference your agents make passes through this loop. The enforcement decision is the only stage in the critical path of the request — everything else runs without blocking. The result is continuous governance with sub-second latency.

02 · Schema

Canonical Evidence Schema

Agent traces from any framework — LangChain, AutoGen, custom stacks — normalize into a single evidence schema. This is what makes cross-system governance possible: one schema, every framework, every model.

The schema is published openly. We don't believe in lock-in on the foundational data layer.

schemas / decision-event.v1.json 
// Canonical decision event — framework agnostic
{
  "decision_id": "9af2c7…b18d",
  "agent":       "credit-assist",
  "framework":   "langchain@0.3",
  "inputs":      { … },
  "retrieval":   [ … ],
  "policy_eval": {
    "obligations": ["POL-029"],
    "verdict":     "route",
    "confidence":  0.71
  },
  "enforcement": "route → human_reviewer",
  "latency_ms":  612,
  "signature":   "ed25519:…"
}

03 · Control Mapping

Canonical Control Schema

Customer control libraries from OneTrust, Archer, ServiceNow, and MetricStream map to a single internal control schema. Policy obligations bind once and apply everywhere.

When your control library changes, your AI governance changes with it — automatically.

External GRC

OneTrust

Archer

ServiceNow GRC

MetricStream

→→→→

Canonical

Internal schema

Canonical Control

control_id · obligation · severity · binding

04 · Foundation

Headless Tracing Backbone

Built on Langfuse (MIT-licensed) as our tracing backbone, so the evidence pipeline is open at the foundation. No vendor lock-in on the most critical layer of the stack.

You can self-host the tracing layer in your own environment, on your own cloud, against your own retention policy.

STACK

LAYER 1 · TRACE

Langfuse · MIT

LAYER 2 · NORMALIZE

Canonical Evidence Schema

LAYER 3 · GOVERN

Policy Engine & Enforcement

LAYER 4 · BIND

Canonical Control Schema

LAYER 5 · INTEGRATE

GRC outflow (OneTrust · Archer · ServiceNow)

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