Validation beta. Planned product capabilities are shown transparently; no payment is taken today.
Use case

Use local models first—escalate to cloud models when the task earns it.

A local/cloud routing pattern for builders who want privacy and cost control without giving up frontier-model quality.

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The operating problem

Why this matters

Local models can handle repetitive or sensitive tasks cheaply, but forcing every job local can reduce quality. Sending everything to frontier cloud models wastes paid capacity and increases exposure. The useful layer is a policy that decides when to stay local and when to escalate.

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.

  • Keep privacy-sensitive or routine work local when capability is sufficient.
  • Escalate to frontier models for high-risk or high-complexity tasks.
  • Prefer already-paid subscription capacity before metered APIs.
  • Log why escalation happened so routing policies can improve.

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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