Evaluation harness
A graded set of real cases running in CI on every prompt, model or retrieval change, so quality regressions are caught before release rather than by a customer.
Industries
AI features you can change without fear
Most startups ship an AI feature quickly and then stop touching it, because nobody can tell whether a change made it better. The fix is unglamorous: evaluation, observability and cost discipline - the engineering practice that turns a demo into a product.
The pressure right now
What we hear from saas & startups
- An AI feature in production with no regression tests and a prompt nobody dares edit
- Inference cost per active user rising faster than revenue per active user
- Enterprise prospects sending AI security questionnaires you answer from scratch each time
- Support volume outgrowing the team while quality drifts
- Model provider changes - deprecations, price rises, behaviour shifts - landing as production incidents
Where it pays off
Six places AI earns its keep here
Not everything on this list will apply to you. Most organisations start with one and extend once it has been measured.
Observability for AI paths
Tracing, cost attribution and failure taxonomy for AI requests, with redaction designed in - traces are where customer data most often leaks.
Support deflection
Grounded answers from your own docs and the account's actual state, with confident escalation and a hard refusal to guess on billing or security questions.
Cost and routing optimisation
Routing by task complexity, caching, prompt trimming and batch handling. Substantial cost reduction with no measurable quality change is a common early result.
Content and lifecycle operations
Documentation, release notes and lifecycle messaging drafted from your actual changelog and product data rather than written from memory.
Security and governance evidence
The model inventory, data flow map, subprocessor list and evaluation evidence that enterprise procurement asks for - assembled once, maintained thereafter.
Where we would start
The first three moves
1
Evals before features
Two or three days of work that unblocks every subsequent change to the AI path.
2
Instrument the cost
Cost per request, per feature and per customer. You cannot price an AI product you cannot measure.
3
Write the buyer pack
The same questions arrive in every enterprise deal; answering them once shortens every cycle after.
Risk and regulation
The part most suppliers skip
Where the risk sits
- Customer data in prompts, logs and third-party tracing tools
- Subprocessor disclosure obligations when you add a model provider
- EU AI Act provider obligations if your product reaches EU users
- Contractual commitments on data residency and training exclusion
- Single-provider dependency as a resilience and commercial risk
How we handle it
Redaction and retention are designed into the logging layer from the start, because retrofitting them after a customer's security review is far more expensive.
Model providers are treated as subprocessors: disclosed, contracted, and swappable behind an interface with fallback routing.
Governance artefacts are produced by the engineering process rather than as a separate document exercise, which is the only reason they stay accurate.
Where this fits
Where to go next
Questions
Questions from saas & startups
The evaluation harness and cost instrumentation are, and they usually take days. The full governance framework is not, and we would tell you to defer it until an enterprise deal or an EU launch makes it necessary.
Yes - that is the usual arrangement. We work in your repository, your CI and your review process, and we leave the patterns behind with your team rather than creating a dependency.
Rarely, at an early stage. Retrieval, prompt discipline and a better model choice almost always get there first, more cheaply and with less maintenance. When fine-tuning is genuinely the answer, the evaluation suite is what proves it.
Start with an audit of what you already run
Two to four weeks to an evidenced picture of your AI use, spend and risk - and a ranked list of what to do first.