Solutions

AI production engineering

Close the gap between a convincing demo and dependable operation.

When this is useful

A prototype may work on selected examples while struggling with real traffic, incomplete inputs, tool failures or unpredictable model costs. We review those operating conditions before recommending changes.

  1. Failure evidence
  2. Evaluation set
  3. System controls
  4. Deployment checks
  5. Monitoring

A focused first proof

Reproduce one meaningful failure and define a measurable acceptance criterion. Compare a targeted improvement against the current behavior, including cost, latency and fallback quality.

Before production

Work can include deterministic validation, evaluations, model routing, caching, queue handling, retries, permissions and observability. We can collaborate with your developers rather than requiring a full rewrite.

Problems this can help address

Engineering in practice

Explore the related Mikisi Labs system, including its current status and architecture.

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