Run & improve
AI in production, watched and made better.
Once an AI system is live, the work changes: inputs drift, models get updated, costs creep. We watch quality, cost and speed against the bar we agreed, catch problems early, and keep making the system better.
An example production monitor: answer quality holds above the agreed bar, starts to drift, the drift is detected, a fix ships, and quality recovers before it crosses the bar.
Recovered, back where it was
What it is
An AI system that passed its evaluations on launch day can quietly get worse — new kinds of requests, a changed document, a provider’s model update. Run & improve is an ongoing engagement: we run your evaluations on real traffic, review what fails, fix it, and show you the numbers every time.
It is the last stage of how we work, built on the evaluations described under Data & evaluation pipelines.
The spec
- What you get
- Continuous evaluation on a sample of real production traffic
- Alerts when quality, cost or speed moves away from the agreed bar
- Regular fixes — prompts, retrieval, data and model updates — each tested before release
- A readable report: what changed, what improved, and what we are watching
- How we measure it
- The bar we agreed before launch — quality on your test set and on sampled live traffic, cost per task, and response time — tracked over time, with every change compared before and after.
- A good first step
- Give us access to the logs of a system that is already live. We turn a week of them into an evaluation set and a first report.
What we watch with
Tooling we commonly use. Where you already have monitoring, we plug into it.
- Evaluation suites
- Production tracing
- Sampled human review
- Drift checks
- Cost dashboards
- Prompt and model versioning
- Alerting
- OpenTelemetry
- Your existing observability stack
Where it fits
Examples of what this work looks like — not client stories.
- Example
A support agent after launch
Escalations and wrong answers are reviewed every week and turned into new test cases and fixes.
- Example
Documents that keep changing
Re-indexing, freshness checks and citation audits, so answers never quote last year’s policy.
- Example
A model provider update
When a provider ships a new model version, we run your evaluations first and switch only if it holds the bar.
- Example
Cost creeping up
Find where the spend goes, then cache, route or distil so cost per task comes back down without losing quality.
Common questions
Ordinary support keeps software running. An AI system can keep running and still get worse. Run & improve is about quality: we measure it continuously and act on it, not only when something breaks.
No. We can take over a system another team built. We start with a review and an evaluation set, so we both know where it stands before we change anything.
We agree it with you up front: the metrics, the test set and the thresholds. When a number moves, we look at the real cases behind it before calling it a problem.
We agree response times and hours with you when we set up the engagement, based on how critical the system is. They are written into the agreement, not promised on a web page.
Is your AI getting better or worse?
Tell us what is running in production. We will tell you how we would measure it, and what we would watch first.