Product engineering

AI-Powered Product Development

Design and build AI-native features inside an existing product, or a new AI-enabled platform, with the delivery discipline of ordinary software.

The problem

Adding AI to a product raises questions most teams have not had to answer before: how to evaluate quality, how to price a feature whose cost varies per use, and how to ship changes without regressing behaviour.

Delivery pipeline

  1. 1

    Commit

    Reviewed, on a branch

  2. 2

    Build and test

    Unit, integration, evaluation

  3. 3

    Scan

    Dependencies and images

  4. 4

    Staging

    Environment matches production

  5. 5

    Progressive rollout

    Weighted traffic, automatic rollback

  6. 6

    Observe

    SLOs, traces, error budget

Every change takes the same route to production, and the rollback path is exercised rather than assumed.

How it works

  1. 1

    We start from a feature with a measurable user outcome rather than from the technology.

  2. 2

    The AI component is built behind an interface, so the model or provider can change without a rewrite.

  3. 3

    Evaluation runs in CI against a golden dataset, gating releases the way any other test would.

  4. 4

    Per-request cost is instrumented from the first day and surfaced alongside usage.

  5. 5

    The feature ships behind a flag, with monitoring on quality, latency and spend.

What you should expect

  • AI features that can be changed safely after launch
  • A quality baseline that regressions are measured against
  • Unit economics understood before scale, not after
  • Provider choices that stay reversible