How do we turn an AI prototype into a reliable product?

Define success beyond the demonstration

Choose a specific task and compare the proposed feature with the existing way of completing it. Include difficult cases, incomplete inputs and situations where the system should ask for help or decline to proceed. Decide who judges a result and what level of error is acceptable for that use.

Test the workflow as well as the model: a technically plausible answer may still be too slow, too expensive or too difficult for the customer to use.

Make the operating decisions explicit

• Data: what information is needed, who may access it and where it goes.
• Evaluation: which representative examples reveal whether a change improves the result.
• Failure handling: how people recognise, correct or recover from a poor result.
• Economics: the cost of a completed useful task, including retries and human review.
• Ownership: who monitors performance, handles incidents and decides when to change the system.

Start with a bounded use case and an observable rollout. Keep a way to disable or revert the feature if its effects differ from expectations.

Connect product, engineering and leadership

My current work with Deepnote includes an AI product transition, engineering standards and enterprise delivery, alongside an achieved 40% reduction in server costs. That saving concerns server costs; it is not a claim about model inference savings.

Read about my Deepnote engagement

The starting conversation is about the product outcome and the organisation’s ability to support it. The useful work may involve product decisions, engineering practice or security and governance.

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