Find it in the documents, not the field.
Machine-speed analysis of technical specifications. Find contradictions, gaps, and duplicates before sign-off.
Find it in the documents, not the field.
Machine-speed analysis of technical specifications. Find contradictions, gaps, and duplicates before sign-off.
Catches the defects your review process missed before they become change requests.
Each layer targets a distinct class of specification defect. Together they cover the full range of issues that generate change requests, integration failures, and audit findings. Specification errors caught before sign-off cost a fraction of what they cost at integration. The same finding on a 1,000-requirement document takes minutes to surface. On a manual review, it may never surface at all.
Every requirement scored 0–10 on Ambiguity, Measurability, Conciseness, and Completeness. Each low score comes with the specific issue and a rewrite suggestion so your team knows exactly what to fix.
A vague requirement is a blank cheque for a supplier to interpret however suits them.
Three classes of conflict: logical negation, numeric constraint, and semantic contradictions — all ranked by severity. The class of finding that only surfaces during integration testing and generates expensive change requests.
Zero false positives on clean specifications: precision is the primary design goal.
Semantically redundant requirements clustered and ranked by similarity — even when written in entirely different vocabulary. Left undetected, duplicates create conflicting contractual obligations when vendors interpret them differently.
The Sherlock engine understands meaning, not just words.
Requirements classified across functional, performance, safety, security, reliability, and compliance domains. Shows exactly where coverage is thin or absent relative to project scope — before contract signing, not after an audit fails.
A specification that looks complete may still be systematically missing an entire domain.
Requirements checked against established industry standards (AUTOSAR supported today; ISO 26262 and ASPICE on the roadmap). Find which standard obligations your specification does not cover before you submit for certification.
Large-scale compliance analysis beyond what manual review can achieve.
Requirements clustered by semantic similarity into neighbourhoods of related content. Every explicit cross-reference appears as a definitive edge. Navigate the full blast radius of any proposed change before making it.
Understand dependencies and risk concentrations across your entire specification.
Diagrams, tables, and images analysed alongside written requirements to surface mismatches between what the document says and what the visuals imply. Every discrepancy mapped back to the affected requirement IDs.
Written and visual artefacts are often authored separately and drift apart over time.
Not a feature list — a set of reasons why the problem required a new approach.
Existing tools enforce structure: mandatory fields, ID format, attribute completeness. They tell you whether a requirement exists. They cannot tell you whether it is any good. Wyzer Detective analyses requirement ecosystems.
The Sherlock engine runs AI only where no deterministic method can resolve the question, then attaches a confidence score and reason to every finding. Results are reproducible run to run — that is what makes it suitable as a quality gate.
A 1,000-requirement document creates roughly one million possible comparisons. Wyzer Detective eliminates impossible pairs before any AI is involved, reducing the candidate set by orders of magnitude.
Export from IBM DOORS, Polarion, Jama, or Codebeamer — upload the ReqIF file and findings are returned within minutes. Same day value, no infrastructure changes.
Questions from engineering teams, procurement leads, and programme managers.
From our engineering blog
Why weak specification foundations create invisible cost that only surfaces at integration, and why deterministic analysis is the only approach that scales to the complexity of modern vehicle programmes.
Tools built on a single foundation model cannot function as a quality gate. What a hybrid deterministic architecture changes about reproducibility and audit readiness.
Upload your first ReqIF file and the Sherlock engine returns findings within minutes. Same day, no integration required.