Answerproof
A vendor-neutral evaluation harness that binds important business questions to governed metric contracts, point-in-time reference queries, datasets, tolerances, and replay evidence, then surfaces answer or schema drift for human review.
AI analyst products can answer the same business question differently after a prompt, model, schema, semantic layer, permission, or source-data change. The supplied research confirms a growing analyst category and general tracing tools, but found no direct product joining registered business questions, reference-query replay, and schema-drift review.
Answerproof should never call one query eternal ground truth. A metric owner defines the question, business meaning, permitted scope, reference query, dataset state, expected shape, tolerance, freshness, and effective version. Scheduled runs capture both the analyst answer and reference result under comparable conditions, then separate execution failure, schema drift, semantic mismatch, numerical divergence, unsupported narrative, and source-data change.
The value is a longitudinal, customer-owned regression corpus and independent receipts. The risk is false confidence: an old or incorrect reference can make the analyst look wrong, while a matching number can hide flawed reasoning. Every failure and pass must remain attributable, challengeable, and bounded to the tested question and dataset.
Data, analytics, and revenue-operations leaders at small and midsize companies deploying conversational analysis against governed business data.
Data and analytics owners deploying AI analysts are directly addressable.
Multiple funded analyst products make independent evaluation timely.
One cross-reference and one inbound connection provide moderate support.
The buyer and failure mode are precise, the analyst category is active, and a customer-owned regression history can become operationally valuable.
Reference queries can be wrong, comparable replay is technically difficult, two proposed interfaces remain unverified, and analyst vendors or observability platforms can extend toward evaluation.
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