Evaluation harnesses
A test suite of real cases with graded expectations, run on every prompt or model change, so quality is a number in CI rather than a hunch after release.
Industries
Ship AI features that survive due diligence
Fintechs are usually not short of AI ambition - they are short of the evidence layer that makes an AI feature sellable to a bank, defensible to the FCA and safe to change on a Friday. That layer is buildable, and it is cheaper to build early.
The pressure right now
What we hear from fintech
- Enterprise buyers sending AI-specific security and governance questionnaires
- An AI feature in production with no regression tests, so nobody dares change the prompt
- Inference costs scaling with usage in a way the unit economics did not anticipate
- EU AI Act obligations landing on you as a provider, not merely as a deployer
- Support volume growing faster than the team, with quality already inconsistent
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.
Support deflection that does not annoy
Grounded answers from your own documentation and account context, with fast escalation and a hard rule against guessing on money questions.
Fraud and risk operations
Case summarisation, evidence assembly and consistent narrative writing for analysts - keeping the decision, and the fairness obligations, with a person.
Onboarding and KYC operations
Document handling and exception triage that scales with signups instead of with headcount.
Cost and routing optimisation
Model routing by task complexity, caching and prompt discipline. Halving inference cost without a measurable quality change is a common early result.
Due-diligence evidence pack
The model inventory, data flows, evaluation results and governance artefacts assembled once and reused in every enterprise sales cycle.
Where we would start
The first three moves
1
Build the evals before the feature
It is the difference between an AI feature you can improve and one you are afraid to touch.
2
Work out your AI Act position
If you provide an AI system into the EU market, obligations attach to you as provider. Knowing your tier early is much cheaper than discovering it in a customer's questionnaire.
3
Assemble the evidence pack once
The same twelve questions arrive in every enterprise deal. Answer them properly once and reuse it.
Risk and regulation
The part most suppliers skip
Where the risk sits
- Provider obligations under the EU AI Act if your product reaches EU users
- FCA expectations where you are regulated or serve regulated clients
- Customer data in prompts, logs and traces - often the weakest point in a fintech stack
- Fairness and explainability in anything affecting credit, pricing or access
- Third-party model dependency as an operational resilience question
How we handle it
Prompt and trace logging is designed with retention and redaction from the start; observability tooling is where customer data most often ends up somewhere it should not be.
Models sit behind an interface with routing and fallback, so a provider outage, deprecation or price change is a configuration event rather than an incident.
The governance artefacts are produced as part of engineering rather than as a document exercise, which is why they stay current.
Where this fits
Where to go next
Questions
Questions from fintech
The full framework is. The evaluation harness, sane logging and a one-page model inventory are not - they take days at your stage and weeks later, and the first enterprise deal will ask for them.
It can. Obligations attach to providers placing AI systems on the EU market and to systems whose output is used in the EU, so a UK company with EU customers is frequently in scope. We establish the position as part of the audit.
That is the normal arrangement with product teams. We bring the evaluation and governance patterns, your team keeps ownership of the product, and we work in your repository and your process.
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.