AI Analytics Reliability Audit
Find the semantic, data-model and guardrail failures that can make your AI analyst confidently wrong.
Approx. 10 working days · Fixed scope · From $5,000
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This audit is a fit if:
- You already have a warehouse or semantic layer.
- You are testing or deploying an AI analyst.
- Business users expect reliable natural-language answers.
- You have real business questions to benchmark.
- You want to understand failure modes before broader rollout.
Every place a valid query can still be the wrong answer.
- question interpretation
- metric selection
- semantic resolution
- grain
- filters
- joins
- answerability
- refusal behavior
- evidence and provenance
- consistency across repeated runs
- dbt project or analytics definitions
- semantic layer or metric definitions, if present
- existing AI analytics architecture
- 25 to 50 representative business questions
- access to a relevant test environment where required
Reliability benchmark
Correct, incorrect, inconsistent, unsupported, should-refuse and silent-error behavior, measured.
Preflight scan
Structural ambiguity: conflicting definitions, hidden scopes, concept forks, grain mismatches, stale or duplicate objects.
Failure map
Every failure classified by source: selection, construction, coverage, ambiguity or runtime safety.
Prioritized repair plan
Each issue mapped to the cheapest correct fix: model, document, declare, enforce, or remove.
Executive readout
Technical report, executive summary, priority matrix and the highest-risk questions.
Focused retest
Where feasible, repair one to three high-value findings, rerun the benchmark, show the before and after.
Benchmark, diagnose, repair, retest.
Not a generic consulting process. A measurement loop: every claim starts from a number and ends at a re-measured one.
- 01
Benchmark
Ask realistic business questions and measure what happens.
- 02
Diagnose
Identify whether the failure comes from selection, construction, coverage, ambiguity or runtime behavior.
- 03
Repair
Fix the cheapest correct layer: modelling, semantics, documentation, declaration or enforcement.
- 04
Retest
Re-run the benchmark and measure what actually changed.
From $5,000
Fixed scope · Approx. 10 working days
A fixed scope agreed up front, from kickoff to readout. The final figure depends on the size of the stack and the number of business questions in scope. You leave with the benchmark, the failure map, and a prioritized repair plan whichever way the numbers come out.
AI Analytics Reliability Sprint
An extension of the Audit: a focused implementation covering semantic modelling, governed metrics, evals, guardrails and agent architecture. Scoped from the highest-risk findings the Audit surfaces.
Can your AI analyst be trusted with your numbers?
Book a short call to work out whether the audit fits your stack, your questions, and where you are in your rollout. No pitch: if it is not a fit, I will tell you.
Having trouble with the calendar above?