Quantitative AI Assurance
The independent verification, benchmarking, runtime control and outcome measurement of AI-generated numerical results and actions.
Verify the result
Identify material numerical claims, units, magnitudes and relations. Recompute against sources and distinguish correction, contradiction, assumption, uncertainty and missing evidence.
Benchmark the model
Measure quantitative reliability using explicit task suites, execution modes, methodology versions, hidden sets and signed result packets.
Guard the decision
Evaluate value, confidence, evidence, policy, liquidity and downside before an AI agent or workflow takes material action.
Prove the outcome
Record what happened, quantify error and bias, and feed observed skill back into selection, governance and operations.
A control plane, not a confidence badge.
Runtime Guard returns an operational decision that downstream systems can enforce.
What assurance establishes.
Why is quantitative AI assurance different from model monitoring?
Monitoring observes systems in production. Quantitative assurance begins earlier by verifying claims, benchmarking reliability and checking actions, then closes the loop with observed outcomes.
What verdicts can LargeQuant issue?
LargeQuant can verify or correct a claim, identify contradictory evidence, request evidence, mark an explicit assumption or return an indeterminate result. Missing evidence is not labeled false.
What can Runtime Guard return?
Runtime Guard can return ALLOW, ALLOW WITH EVIDENCE, CHALLENGE, REVIEW, BLOCK or NEEDS EVIDENCE according to the active policy and evidence.