Does your AI’s reasoning hold up?

ReasonIQ tests whether an AI system’s reasoning would survive scrutiny from an expert, an auditor, or a regulator, not just whether its answers look right.

ReasonIQ

Decision-intelligence platform

An AI reasoning and decision-intelligence platform designed to evaluate and improve complex reasoning capabilities.

The problem. Organizations increasingly rely on AI for decisions with real consequences: underwriting, forecasting, triage, resource allocation. Most evaluation methods check whether the final answer looks right. They don’t check whether the reasoning behind it would survive scrutiny from an expert, an auditor, or a regulator.

Who it’s for. Teams deploying AI into workflows where a plausible-sounding but wrong conclusion is expensive: risk, finance, operations, and product organizations building AI-assisted decision tools.

Why conventional evaluation falls short. Accuracy benchmarks reward the right answer for any reason. They miss reasoning that is fragile, shortcut-prone, or sensitive to how a problem happens to be phrased, which are exactly the failure modes that surface later, in production, on cases the benchmark never saw.

Key capabilities

Structured evaluation of reasoning chains, not just final outputs
Stress-testing against rephrasing, missing context, and adversarial framing
Traceable reasoning reports suitable for internal review or audit
Benchmarking across model versions as systems are upgraded

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Try it: same answers, different graders

Two AI systems answered the same 200 questions once. Their answers never change below; only the AI grader marking them does. Pick a grader and watch the scores, and the winner, move.

System A writes longer answers

System B gives more cautious answers

Scores under all five graders

Want to know how much your own AI system’s scores depend on the grader?

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Illustrative data, simulated to show the effect. These are not results from any real AI system, grader, or client.

Example: health AI benchmark audits

Health AI companies increasingly compete on benchmark scores, and investors, hospitals, and journal reviewers rely on them. Small choices, such as which AI model grades the answers, which settings were on during the test, or whether the raw results were kept, can move a headline number by several points. ReasonIQ audits published health AI results at the level of code, data, and statistics, and reports which claims hold, which are fragile, and which can’t be verified.

Reproducibility: re-running a stratified sample end to end and comparing against the published scores
Grader sensitivity: re-grading identical answers with several AI graders to see how much the score depends on the choice
Grading variance: repeating the grading to separate real performance from grader randomness
Statistics: confidence intervals, paired comparisons, and correction for running many tests at once
Test conditions: whether benchmark runs used different settings, code paths, or safety features than the real product
Contamination: whether benchmark questions leaked into training data or the retrieval corpus
Robustness: whether scores survive reworded questions that mean the same thing
Provenance: whether every headline number traces back to item-level data that still exists

Scope
We agree which published claims are in scope and collect the code, data, results files, and exact run settings behind each one.
Reproduce
We re-run a stratified sample independently, with every setting logged, and compare the results item by item against what was published.
Stress-test
We re-grade with several graders, repeat grading, check for contamination and rewording effects, and re-derive the statistics.
Report
You receive a findings report rating each claim by confidence, with the evidence behind every rating and the fixes that would close each gap.

Ask about a health AI audit

Audits and publication support are offered as separate engagements, so an audit’s findings never depend on helping the same results get published.

Example: risk, validating AI in credit decisions

Lenders increasingly use AI to read financial statements and bank records, draft credit memos, and recommend whether to approve a loan. A recommendation can be right for the wrong reason: it may cite a ratio it calculated incorrectly, overlook a covenant breach, or change its answer when the same borrower is described differently. ReasonIQ tests the reasoning behind each recommendation, so credit committees, internal auditors, and regulators can see why the AI concluded what it did, and whether that reasoning holds.

Calculation checks: every ratio the AI cites, such as debt service coverage or leverage, recomputed from the source statements
Evidence tracing: each claim in the credit memo traced back to the document and page it came from
Consistency: the same borrower, presented in a different order, format, or wording, should get the same recommendation
Missing information: whether the AI flags gaps, like a missing year of accounts, or quietly fills them with assumptions
Fairness: whether the recommendation shifts when factors that shouldn’t matter, like a name or location, are changed
Policy adherence: every recommendation checked against the lender’s own credit policy and thresholds
Version drift: the same case files re-run whenever the underlying model is upgraded
Audit trail: a reasoning report for each decision, ready for credit committee or audit review

Ask about ReasonIQ for lending

Example: investment banking, checking AI-drafted valuation and deal materials

AI increasingly drafts pitchbooks, information memoranda, and valuation commentary. The risk is a polished narrative that doesn’t match the numbers, such as a summary that calls a company undervalued when the model says otherwise, or peer figures that appear in no filing. ReasonIQ checks whether each conclusion follows from the underlying analysis, traces every figure back to its source document, and tests whether conclusions shift sensibly when inputs such as the peer set or key assumptions change.

Ask about ReasonIQ for deal teams

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