Correct facts. Distorted evidence.
Individually true facts can still mislead when counterevidence is omitted, retrieval is selectively framed or the evidence set is unrepresentative.
Epistemic assurance for consequential AI
When an AI answer looks right, what proves it should be trusted?
EIAS Epistemic Integrity Scan adversarially evaluates consequential LLM and agent outputs for failures that can survive ordinary factuality, security and policy checks.
Secure is not the same as supported. Cited is not the same as entailed. Agreement is not the same as independent corroboration.
The failure can be upstream
A plausible answer can still be epistemically unfit for release.
Real citation. Unsupported claim.
A source can exist and still fail to support the claim placed beside it. EIAS separates citation presence from claim-evidence entailment.
Multiple judges. One evidence lineage.
Agreement does not automatically increase confidence when evaluators share retrieval, sources, assumptions or correlated failure modes.
Epistemic Failure Cascade
Find the first material failure before it propagates.
An unresolved upstream defect is not repaired by confident wording, more citations or a downstream human-review disclaimer.
Premise bias
Missing counterevidence
Evidence-selection distortion
Claim-evidence / inference failure
False consensus / evaluator dependence
Confidence inflation
Premature action authority
Release-integrity failure
The engagement contract
What you provide. What you receive.
The customer provides
- Representative AI or agent traces
- Prompts and outputs
- Retrieved evidence where available
- Tool-call records where available
- Existing PASS/FAIL or review status
- Workflow context, decision owner and consequence boundary
The customer receives
- Executive integrity scorecard
- Material failure register
- First-failure localization
- Claim-evidence findings
- False-positive / false-negative review where adjudication permits
- Control-gap recommendations
- Evidence-backed dispositions
- Audit-oriented evidence pack
The exact scope depends on evidence availability. Missing trace data must be declared, not silently reconstructed.
Available without a runtime deployment
Start with the traces. Automate only what proves useful.
AI / agent traces↓
Human + LLM + EIAS v7.0↓
Adversarial replay↓
Independent / human adjudication where material↓
Integrity disposition↓
Evidence pack
EIAS can be delivered as a bounded assurance engagement before any production sidecar or runtime integration. Repeated controls may later be automated only after evaluation demonstrates that the automation preserves decision quality.
Integrity dispositions
Six honest ways for a scan to conclude.
The objective is not maximal refusal. It is the strongest useful conclusion and action that the available evidence, uncertainty, independence and authority actually justify.
RELEASEQUALIFYCHALLENGEESCALATEABSTAINHOLD
Product maturity
Designed for evaluation. Not represented as empirically proven.
EIAS v7.0 is a self-contained operating specification. Its empirical effectiveness remains unvalidated. BlueprintStrategies.AI does not claim validated false-positive or false-negative rates, production superiority, regulatory certification or guaranteed outcomes without controlled evidence. Controlled benchmark development and adversarial evaluation are the next validation boundary.
Start with one bounded workflow
Which AI decisions are already passing your controls?
Select a representative trace set and define the consequence boundary. The first engagement should establish whether EIAS identifies material integrity defects, assurance evidence or control gaps that matter to the decision owner.