AI development in London
A London-registered engineering company building AI and machine-learning systems for UK businesses. Senior engineers in your timezone and jurisdiction, who will tell you where AI genuinely applies and where it does not.
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London
AI Development for London
Yarqat is registered in London, and it is our home market, which means AI work here comes with something the offshore route cannot offer: engineers you can get in a room. For UK fintech deciding where a model belongs in an underwriting flow, for healthcare handling sensitive patient data under real regulation, and for enterprises with years of data trapped in systems that were never built to share it, the hard part of AI is rarely the model. It is the questions around it, and those are far easier to work through in person than over a twelve-hour lag.
The most useful thing we bring to an AI conversation is a willingness to say no. A great deal of what gets pitched as AI is better served by a rule, a search index, or simply cleaning up the data first. When AI does apply, the projects that fail almost never fail on the model. They fail on data access, because the information the model needs is locked in a system nobody can cleanly export from, and on evaluation, because there was never an honest way to measure whether the output was good enough to put in front of a customer or a clinician. Our senior engineers start there.
Because we are here, the practical friction disappears. Contracts under English law, sterling invoicing, working hours that match, and the option of an in-person workshop when a decision about sensitive data or a model boundary genuinely needs everyone at one table. For UK fintech and healthcare especially, being able to sit down with the people handling the data, rather than briefing them across a timezone, is often what turns an AI idea into something that can actually ship.
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What is specific
Built for London
We will tell you where AI does not apply
The most valuable answer is often that you do not need a model. A rule, a search index, or better data plumbing frequently beats a model that costs more to run than the value it returns. Senior engineers who scope the work honestly will say so, because we would rather build you the right thing than the fashionable one, and in London we can have that conversation face to face.
Evaluation and data-access realism
Before model choice we ask the two questions that actually decide the outcome: can we cleanly get at the data the model needs, and how will we measure whether the output is good enough to trust. UK enterprise data is usually scattered across older systems, so we plan the access and the evaluation up front rather than discovering the gap halfway through the build.
Sensitive data, worked through in person
UK fintech and healthcare AI touches data with real regulatory weight, and the decisions about what a model may see, where it runs, and who reviews its output are best made with everyone in the room. Being London-based means that is a train, not a flight, so an in-person workshop is a genuine option when the stakes justify it.
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Scope
What we build
Project delivery, owned end to end, for London clients.
- LLM-powered features such as assistants, drafting and internal search, with guardrails and a human fallback where it matters
- Document processing and extraction over contracts, records and enterprise data trapped in older systems
- Retrieval systems over your own documents, with clear boundaries on what sensitive data a model may see
- Forecasting, classification and decision-support models for UK fintech and operations
- Computer vision and image or document understanding where the use case genuinely warrants it
- A rigorous evaluation harness measuring accuracy, failure modes and production cost before anything ships
AI Development in London: common questions
Where is Yarqat based, and can we meet in person?
Yarqat is a UK company registered in London, and it is our home market. That means the same timezone and legal jurisdiction as our UK clients, and the genuine option of an in-person workshop when a decision about sensitive data or a model boundary calls for everyone in one room. We default to remote for most of the build, because that is genuinely how most of the work is best done.
How do you decide whether AI is the right approach?
We start by trying to talk you out of it. A lot of what gets framed as AI is better solved by a rule, a search index, or fixing the data first. When a model genuinely earns its place we build it, but a senior engineer telling you where AI does not apply will save you far more than one who says yes to everything.
Can you work with sensitive or regulated data?
Yes, and being in the UK matters here. Decisions about what a model may see, where inference runs, and who reviews its output are best worked through directly with the people who hold the data, which for UK fintech and healthcare clients we can do in person. Those boundaries shape the architecture from the start rather than being negotiated near launch.
Why do AI projects fail, and how do you avoid it?
Rarely on the model. They fail because the data the model needed was locked in a system nobody could cleanly export from, or because there was never an honest way to measure whether the output was good enough. We plan data access and evaluation before we choose a model, which is unglamorous and exactly why it works.
How do you keep AI running costs under control?
By treating cost per request as part of evaluation rather than a surprise after launch. Model choice, prompt size, caching and where inference runs all move the bill, so we make those trade-offs deliberately and show you the numbers before you commit to shipping.
AI development in London?
A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.