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Production Readiness Scorecard

Will your AI pilot survive production?

Twelve questions across the six places pilots actually break. It takes about three minutes, and every answer comes from what your team already knows — there are no industry averages or assumed benchmarks in the result.

0 of 12 answered

  1. 01 · Data & Retrieval

    Can the system reach the data it needs in production, with the same permissions a real user has?

  2. 02 · Data & Retrieval

    Do you know how often retrieval returns the right context?

  3. 03 · Evaluation

    Is there a fixed test set you re-run before every change?

  4. 04 · Evaluation

    How do you define a successful response?

  5. 05 · Cost & Observability

    Do you know what one successful task costs, including retries and failures?

  6. 06 · Cost & Observability

    Can you attribute spend to a feature, customer, or workflow?

  7. 07 · Latency & Reliability

    Is there a latency budget the feature has to meet?

  8. 08 · Latency & Reliability

    What happens when the model call fails or times out?

  9. 09 · Safety & Control

    When the system changes state — sends, writes, pays, files — who approves it?

  10. 10 · Safety & Control

    Are the system’s tools and permissions scoped to the minimum it needs?

  11. 11 · Ownership

    Who owns this once it is live?

  12. 12 · Ownership

    If a model or prompt change degrades quality, how fast can you revert?

Your Result

Answer all 12 questions to see your score and the dimensions most likely to break first. 12 to go.

Why These Twelve

Most AI initiatives fail at the integration layer, not the model layer.

Every question above sits in that layer. None of them are about which model you chose, because in our experience that is rarely what stops a launch. What stops a launch is retrieval nobody measured, cost nobody attributed, failures nobody scoped, and ownership nobody agreed.

A Product Audit goes through the same six dimensions properly, with your code and your data, over two to four weeks.

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