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From standards and specifications to structured, traceable, and automation-ready tests.
TCG is an AI-powered application from Understand Tech designed to accelerate the path from technical specification to executable testing assets. It ingests standards and technical documents, extracts requirements with traceability, generates test cases in a controlled format, analyzes quality and coverage, and can optionally produce executable scripts aligned with engineering conventions.

TCG is a specialized application for teams working with standards, specifications, and requirement-heavy documents. Instead of manually turning dense source material into test suites, teams can use TCG to generate structured test artifacts grounded in the specification, with measurable quality signals and review support built in.
When a specification gets updated, you don't want to regenerate every test case from scratch. TCG's Delta Generation detects exactly what changed between two versions and updates only the impacted test cases.
Not sure what that means in practice? Listen to Naama, our CEO, walk you through it in under a minute.
A structured pipeline from specification to automation-ready output.
TCG starts from the source material, typically a technical specification in PDF or related formats. The system processes the document structure, including sections, tables, figures, and annexes, rather than relying only on plain-text extraction.

The application extracts requirements with IDs, metadata, references, modality signals such as MUST, SHALL, or SHOULD, and traceability back to source sections. It also captures relevant surrounding context such as definitions, prerequisites, and exceptions.

Using extracted requirements, source references, metadata, and a user-defined test format, TCG generates structured test cases aligned with the requested template and wording style. It can also use curated knowledge base context to improve grounding.

Generated results are analyzed for structure and AI quality. TCG identifies issues such as duplicates, missing validation, weak traceability, or low-confidence outputs, and produces a guided fix plan showing what is safe to apply automatically versus what should be reviewed.

Where needed, TCG can transform approved test cases into executable scripts using generation profiles, framework selection, repo-aware conventions, and preflight checks. Outputs can be exported as automation-ready bundles.

Parses specifications into a structured hierarchy and extracts normalized requirements with IDs, metadata, modality, references, and traceability links.
Test cases are generated in the exact user-defined format, including expected structure, fields, numbering, and wording conventions.
Combines deterministic checks with AI review. Deterministic checks address structure, duplicates, modality consistency, while LLM review identifies ambiguity and weak intent alignment.
Produces confidence-related metrics per test case, including signals around faithfulness, intent match, and contextual relevance.
Provides visibility into requirement and section coverage, helping teams identify missing, weak, or redundant testing areas.
Approved outputs can be converted into executable scripts using framework-aware generation and project-aligned conventions, with preflight checks.
Short videos showing what TCG does in practice, from requirements to generated tests.
TCG uses a pipeline approach rather than a single prompt-response pattern. It builds a structured understanding of the source specification, extracts normalized requirements, generates test cases under template and context constraints, and evaluates output quality through both deterministic checks and AI-based review signals.
For teams working on standards conformance, certification-oriented testing, or requirement-heavy validation programs, TCG preserves engineering discipline while reducing manual authoring and maintenance effort.
TCG does not stop at test case drafting. For teams that want to accelerate automation, the application can generate executable scripts using selected frameworks and generation profiles aligned with project conventions.
Generation accounts for repository patterns, allowed imports, helper usage, and enterprise constraints. Preflight checks help prevent or flag scripts derived from low-trust or insufficiently validated test cases.
TCG supports the elements reviewers typically expect from a "ready" conformance suite.
Teams working with requirement-heavy technical documentation.
AI generates a strong first draft of the test suite from the specification, reducing repetitive manual drafting work.
Using a standard template and generation controls helps create more uniform outputs across teams and document sets.
When requirements evolve, teams can update or regenerate instead of rewriting large parts of the suite from scratch.
Quality signals, traceability, and guided fixes help reviewers focus attention where it is most needed.
Optional script generation reduces the manual step between approved test case and executable test asset.
What TCG does, how it keeps tests traceable, and where it can run.
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