Requirements engineers were manually validating every requirement against INCOSE standards - a slow, inconsistent, and error-prone process that scaled poorly as engineering throughput demands grew. Variability between reviewers meant quality was never fully consistent, issues were caught late in the process, and there was no auditable trail linking validation decisions back to specific rules. As project complexity increased, the manual model became a growing bottleneck and risk.
Unframe deployed an AI requirements engineering platform tailored to the company's systems engineering workflows - giving requirements engineers automated INCOSE rule validation, confidence-based verdicts, and a human-in-the-loop review interface for edge cases. The solution ingests requirements files and glossary definitions, applies a configurable INCOSE rule engine to each requirement, and generates a pass/fail verdict with clear reasoning and suggested corrections. High-risk evaluations are automatically flagged for human review rather than auto-corrected, preserving engineering judgment where it matters most. Engineers can edit requirements directly in the platform and re-run validation instantly. All results are exportable to Excel with full traceability back to the specific rules applied.
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