AI engineering, United Kingdom
Most AI projects fail in production, not in the demo.
We build the part that comes after the prototype. Evaluation, guardrails, retrieval that cites its sources, pipelines that survive a retry, and a handover your own engineers can pick up.
Capabilities
Five things, done properly.
Each of these is a discipline rather than a feature. If what you need is not on this list, the honest answer is usually that somebody else is better placed.
LLM integration
Putting a language model behind a real feature, with the evaluation, guardrails and cost controls that stop it embarrassing you in production.
Retrieval and semantic search
Search over your own documents that returns the right passage and cites where it came from, rather than a confident paragraph you cannot verify.
Agent and workflow pipelines
Multi step processes where a model calls real tools against real systems, built so a failure halfway through does not leave your data in pieces.
Model deployment and inference
Running models on your own infrastructure when data residency, unit cost or latency make a hosted API the wrong answer.
Data engineering for AI
The unglamorous work that decides whether anything above will function. Pipelines, quality, lineage and the schema nobody wrote down.
Three ways to start.
Every one of them produces something you own and could hand to another supplier tomorrow.
One to two weeks
Technical discovery
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.
Typically six to twelve weeks
Build
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.
Ongoing, monthly
Embedded
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.
Scope
What we will turn down.
Saying this in public costs some enquiries and saves everybody the ones that were never going to work.
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Chatbots on a website with nothing behind them
If it cannot reach your systems it is a search box with worse manners.
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Training a model from scratch
Almost nobody needs this. It is nearly always a retrieval problem wearing an expensive hat.
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Projects with no way to measure whether they worked
If nobody can say what good looks like, the build will be argued about instead of shipped.
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Replacing a team with a pipeline
We build things that remove work, not things that quietly remove people. Your call, but not our build.
Questions
If yours is not here, email hello@aiengineeruk.com and it will reach a person.
Are you an agency or a contractor?
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.
Do you work with our existing team?
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.
Can you sign an NDA and work under our security requirements?
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.
What if the honest answer is that we do not need AI?
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.
Which providers do you use?
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.
Tell us what you are trying to build.
Technical detail welcome. The more concrete the problem, the more useful the first reply.