Cascade
In enterprise software, a single change request rarely affects just one place. A "small" dashboard tweak can ripple into backend logic, data models, the test suite, QA scope, and release notes; but teams discover that impact piecemeal and too late: the analyst catches the spec, the developer hits conflicts mid-sprint, and QA finds the missing test cases only after something breaks. The cost is rework, blown deadlines, and unclear ownership. Cascade fixes this by mapping the ripple upfront. First, it ingests any GitHub repository and builds a queryable knowledge graph — every file classified into ten artifact buckets through multi-signal inference (never folder names), with structural edges like imports and test-to-module links parsed deterministically and enriched by an LLM. Then a team of Band-coordinated AI agents reasons over that graph. The result: impact analysis that took a whole sprint, delivered in one screen.
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