Start an AI Observability Consultancy

1. 1

   Master the observability stack

   Tracing, logging, evaluation, and monitoring tools for AI systems, plus how to define good metrics. Deep tool knowledge is the service.

2. 2

   Build a reference implementation

   A demo app instrumented end to end with tracing, evals, and dashboards. A working reference is what convinces technical buyers.

3. 3

   Define what to measure

   Latency, cost, quality, and failure modes that matter for each client's use case. Choosing the right metrics is the expert judgment they pay for.

4. 4

   Sell scoped implementations

   A fixed-scope observability setup or a production-readiness review, priced against the cost of AI failures. Clear scope reassures buyers.

5. 5

   Find teams already in production

   Companies with live AI features feel the pain of flying blind. They are your entire market.

6. 6

   Convert to ongoing work

   Once instrumented, teams want ongoing tuning, evals, and reliability work. The setup opens the retainer.

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