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.
Not a wrapper on a generic LLM. Sherlock is a purpose-built analysis engine — trained specifically to interpret engineering requirements and designed from the ground up for accuracy, consistency, and trust. The hybrid AI and rule-based architecture means every finding is traceable, reproducible, and explainable.
From upload to findings in minutes — no integration, no training data, no configuration.
Sherlock accepts industry-standard formats — ReqIF, Word, Excel, CSV — and parses them into a structured requirement graph. No pre-processing, no templates, no manual tagging required.
Each requirement is scored across four quality dimensions: Ambiguity, Measurability, Conciseness, and Completeness. The requirement graph is then interrogated for logical, numeric, and semantic contradictions between requirements.
Sherlock maps your requirement set against domain models for functional, safety, compliance, and security coverage. Gaps are identified, ranked by severity, and linked to the specific area of the specification they affect.
Findings are returned as a structured, navigable report — each issue with a severity rating, a plain-language description, and a suggested rewrite. The same run on the same input always produces the same output.
A 1,000-requirement document creates roughly one million possible pairwise comparisons. A generic LLM cannot process all of them — it loses context, skips pairs, and produces inconsistent results run to run.
Sherlock uses semantic similarity to eliminate pairs that cannot conflict before any AI is involved — reducing the candidate set by orders of magnitude and making the analysis tractable at any scale, consistently.
Every requirement is scored 0–10 across four axes. Each score comes with the specific issues found and a rewrite suggestion.
Identifies vague qualifiers, undefined terms, and language that allows multiple valid interpretations.
Designed from the ground up for engineering rigour — not a general-purpose AI wrapper.
Sherlock combines large language model analysis with a deterministic rule layer. The LLM handles semantic understanding; the rule layer validates logical and numeric consistency. Every finding is traceable to a specific check.
A quality gate that produces different results run to run is not a quality gate. Sherlock's deterministic layer ensures that the same input always produces the same findings — essential for audits, supplier reviews, and gate reviews.
Every issue comes with a plain-language description of what was found, why it matters, and a specific rewrite suggestion. Sherlock does not produce unexplained scores — every number has a reason behind it.
Sherlock only reports on what is present in your specification. It does not generate new requirements or infer missing content beyond what the coverage model can confirm — reducing noise and maintaining engineering trust.
Upload a ReqIF file and receive a full Sherlock analysis — quality scores, contradictions, and coverage gaps — within minutes.