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Use case

Stop rebuilding context every time you switch AI tools.

A proposed handoff layer for teams that plan in Claude, execute in Codex, review in Gemini and use local models for lower-value work.

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The operating problem

Why this matters

Multi-model workflows can be powerful, but the handoff is usually manual: copy prompts, restate constraints, reattach files and hope the next model understands the project. That wastes tokens and creates inconsistent decisions.

Operating pattern

A smaller, measurable control loop.

1. Classify

Understand task risk, privacy, quality needs and the available AI stack before choosing a model.

2. Route

Apply explicit policy instead of defaulting every task to the most expensive or most familiar tool.

3. Measure

Track useful outcomes, spend and exceptions so the routing policy improves from evidence.

  • Define one shared project brief and task contract.
  • Route each stage to the model suited to that job.
  • Carry decisions and acceptance criteria across model handoffs.
  • Escalate only when quality, risk or uncertainty justifies it.

Is this problem worth solving for you?

Register your stack, biggest pain and reasonable price. That evidence determines what we build next.

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