AI inside the workflow, not bolted on.
AI earns its place by doing a defined job inside a real workflow. We embed models where people classify, explain, and draft — then test the output against real cases before it reaches production.
Where AI can carry real weight.
Your team answers the same question all day
Classifying items, checking codes, explaining results — the same judgement, repeated hundreds of times, by people who have better things to do.
You tried a chatbot and quietly turned it off
It was confidently wrong just often enough that nobody dared rely on it. Nobody could tell you how often, because nobody was measuring.
Your customers can't read what you send them
Reports, results, and documents written for specialists land in the hands of ordinary people, who then phone you to ask what they mean.
Research and admin eat the week
Someone senior spends their days gathering, summarising, and re-keying — work an automated agent could do overnight, if anyone wired one in.
Measured in production, not in a demo.
National customs search, from 15% to 96.5% correct
On a UK government tax & customs platform, search was returning the right classification 15% of the time. We built an in-house LLM evaluation framework — so every change to the model or the prompts was scored against ground truth before it shipped — and took correct retrieval to 96.5%.
Outcome: the system now handles around 280,000 classifications a month. We also published a public tariff-data connector on an AI assistant marketplace, so national customs data can be queried from inside AI assistants.
Lab results patients can actually read
In the clinical laboratory system we built and operate for a private clinic in Southeast Europe, every validated result can be explained in plain language by an AI assistant — inside the same workflow that produces the printable report, not in a separate tool the patient has to find.
Outcome: patients get a plain-language explanation alongside the report, without a phone call to the lab. Explanations are generated only from results the laboratory has already validated, and are labelled as explanations — the assistant does not diagnose.
Predictive insight across a global health network
At a global health network, our team led delivery of an AI patient-intelligence platform combining privacy-protected health and consumer data into predictive insight, built on a HIPAA-compliant data partnership.
Why it matters here: AI in regulated settings is a delivery discipline — privacy architecture first, model second.
Prove one decision before automating the workflow.
We choose one repeatable task, define what "right" means, and ship it inside the existing workflow — paid, scoped, and measured. You see performance on real cases before deciding whether to expand. And if you'd rather co-own than commission — you bring the domain, we bring the build — see how we partner.
If a model is going to sit inside your workflow, it should earn its place.
Tell us the decision your team repeats all day, and what a right answer looks like. We reply within two business days — hello@understandata.com.
Start with one decision