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Accelerating Change Propagation Across a Product Family
An industrial equipment maker changes a shared interface dimension and must propagate that change across forty derivative designs built on it.
Runs onShilpaHow the work runs
The pressure that made this worth automating, the steps the system runs, and what came out the other side.
Pressure & Trigger Points
- Finding every derivative affected by a shared change is manual and frequently incomplete.
- Each propagation is modelled by hand, so the same change is implemented forty slightly different ways.
- Missed derivatives surface later as fit problems in the field.
The run · 5 operational steps
Click any step to inspect telemetry signals, model reasoning, and governance gates.
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1
Dependency Discovery
The design graph is traversed to find every derivative that inherits the changed interface.
Input Signal:
Real-time operational telemetry & queue
Reasoning Pattern:
MCP grounded vector inference
Governance Gate:
Policy constrained with audit write-back
Verified Business Outcomes
- Complete discovery of affected derivatives rather than best-effort search.
- One change implemented consistently across the family.
- Fit problems caught before release instead of in the field.
Capabilities this relies on
- generative design
- system connectors
- root cause reasoning
- human approval
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