LLM INTEGRATION · RETRIEVAL · AGENTS · DATA
From your first prototype to a system your team can rely on. AI integration, agents and data pipelines, built for production and handed over properly.
Our capabilities
The engineering behind a useful AI product.
Built, tested and documented for your team.
Language models connected to your product, with evaluation and cost controls.
ExploreAnswers from your documents, with sources your team can check.
ExploreTools and workflows that run safely, recover and leave a clear trace.
ExploreModels on your infrastructure, sized for your data, budget and workload.
ExploreReliable pipelines, clean records and data you can trace to its source.
ExploreEngineering with accountability
You deal directly with the engineer doing the work, from the first technical conversation to the final handover.
Speak to the engineer building it
Evaluation against your real inputs
Your repositories and infrastructure
Documentation your team can use
Working together
Validate an idea, deliver a defined build or bring AI engineering experience into your existing team.
How we workOne to two weeks
A written assessment of whether the thing you want is feasible, what it would take, and where it will break. Delivered as a document you own and can take to another supplier.
You have a business case and need it pressure tested before committing budget.
Explore technical discoveryTypically six to twelve weeks
Scoped delivery against the discovery output. Fixed scope, working software, tests, documentation and a handover your own team can pick up.
The decision is made and you need it built by someone who has shipped it before.
Explore buildOngoing, monthly
Working inside your team on your board. Code review, architecture, and levelling up your engineers so the dependency ends.
You have engineers but nobody who has taken an AI system to production.
Explore embeddedBeyond the prototype
A convincing demo is a beginning. We build the evaluation, guardrails and infrastructure that let you put it to work with confidence.
Explore our engineering stackMeasure quality against your own inputs, so regressions are visible before your customers find them.
Safe retries, execution traces and cost ceilings make the system manageable in production.
Working code, tests and documentation in your repositories, with a handover your engineers can pick up.
Before we talk
Have a specific question about your project? Email hello@aiengineeruk.com and it will reach a person.
Neither description is quite right. Engagements are led by the engineer who does the work, so the person who scopes it is the person who writes it. That caps how much runs at once, which is the trade.
Usually, yes, and it is the better outcome. The embedded engagement exists specifically to leave your engineers able to maintain what was built, rather than leaving you dependent on us.
Yes. Where data cannot leave your infrastructure, that constrains the architecture rather than ruling the project out, and it is far cheaper to design for at the start than to retrofit.
Then you will be told that, in writing, during discovery. A surprising share of the problems that arrive described as AI problems are reporting problems, a missing index, or two systems that disagree about the same record.
Whichever fits the constraint. Everything is built behind an abstraction so switching provider is a configuration change rather than a rewrite, because pricing and capability in this field move faster than any project plan.
Let's work together
Tell us about the system, the constraint and what you have already tried. Technical detail is welcome.