AI & automation
A convincing AI demo takes an afternoon; a system you can leave running is a different discipline. We find the workflows where automation pays, build it with the guardrails and human oversight the stakes demand, and measure the output continuously - because unmeasured AI degrades quietly.
Service details
At a glance
- Workflow assessment before any build
- Production systems, not perpetual pilots
- Evaluation harnesses that prove output quality
- Human-in-the-loop where the stakes are high
The workflows that usually pay first
The best first candidates share three traits: volume enough to matter, a written policy behind the decision, and an output someone can check. In practice that means the unglamorous middle of the business - triaging inbound email and tickets, extracting data from invoices, claims and contracts, drafting the routine document a person then approves, answering the questions your team answers forty times a week. It rarely means the flashy do-everything assistant, and that is precisely why the wins are durable: boring workflows have pass marks.
- Inbound triage - email, support tickets, claims, applications
- Document and data extraction from PDFs and free text
- Drafts for human approval - replies, reports, summaries
- Internal Q&A over the documents your team currently searches by hand
The gap between demo and production
Everyone has seen the demo that dazzled a boardroom and never shipped. The hard part of AI is not getting a good answer once - it is getting acceptable answers on messy, real inputs, month after month, without embarrassing anyone. That second thing is what we build, and it starts with choosing problems where AI belongs at all.
Where automation pays - and where it doesn’t
We look at your workflows with a cold eye: which have the volume, the tolerance for error and the clear success criteria that automation needs, and which are cheaper left with a human. Promising candidates get prototyped and measured against a quality bar you agree to. If a case does not clear the bar, we say so before you have spent real money on it.
- Assessment grounded in your real workflows
- Prototypes measured against an agreed quality bar
- A straight answer where AI is not the tool
How an engagement runs
It starts with a short, fixed-fee assessment of your workflows - where the volume is, what the error tolerance is, what a right answer even looks like. Then one workflow, chosen because it can be measured. We grade a set of real historical cases before building anything, so the quality bar exists before the system that has to clear it. The build goes live behind a flag, with a person handling everything the system is not confident about and every hand-off logged. Only when the numbers hold does the scope widen - to more of the queue, then to the next workflow.
- A fixed-fee workflow assessment before any build
- The quality bar built before the feature
- Live behind a flag, with a person behind every uncertain call
- Scope widens only when the numbers say it should
When we’ll tell you not to use AI
Some of the most valuable advice in this field is negative. If the input has a fixed structure, a parser is right or it stops - a model is only ever plausible, and plausible is dangerous anywhere near a ledger. If the volume is low, a person is cheaper. If nobody can say what a right answer looks like, no system can be signed off, whatever it is built on. We have written a two-hundred-line parser to replace a model we were being paid to build; the client got the reliable version, and we kept the lesson.
Read the feature that never needed a model →Trustworthy in production
Nothing goes live here without an evaluation harness and monitoring, because model output drifts and the failures are quiet. Where a mistake would be costly, a human stays in the loop by design. The result is automation you can leave alone - with the numbers to prove it is still behaving.
Agentic automation, used soberly
The current generation of AI can do more than answer - it can carry a multi-step workflow: read the inbox, pull the record, draft the response, file the update. Built well, agentic automation absorbs work that single-shot AI never could. Built carelessly, it compounds errors across every step it touches. Our rule is the same one that governs everything here: each step the agent takes must be checkable, the risky ones must be reversible or approved by a person, and the whole run must be logged well enough to answer “why did it do that?” - because someone will ask.
What it costs
The assessment is a fixed fee with a two-week turnaround. A first automation is typically a fixed-scope project from £8,000 - deliberately small, because the point is to prove value on one measurable workflow before spending on the next. Where automation becomes a programme rather than a project, the work runs on an embedded team from £4,500 a month per engineer. At every stage you are paying against numbers - deflection, accuracy, hours returned to the team - not against promises.
Five weeks to production, after nine months of pilots
A UK retail group came to us after three vendor pilots had demoed beautifully and shipped nothing. We cut the ambition down to one workflow - returns triage - graded a set of real historical cases before building, and put a person behind every call the model was not sure about. It was live behind a flag in five weeks. A quarter later it was clearing 62% of the returns queue on its own, and the team knew exactly which kinds of case it still could not be trusted with.
Read the full case study →Frequently asked questions
- How do we know AI is right for our problem?
- We start by assessing your workflows and will tell you plainly where automation pays and where it does not. A pilot that goes nowhere helps neither of us.
- How is this different from a flashy demo?
- A demo works once, on friendly inputs. Production means evaluation, monitoring and guardrails, so the system keeps working on the messy inputs real work produces.
- Which AI models and providers do you use?
- Whichever fit the task, the budget and your privacy requirements. We design so you can switch providers later - model lock-in ages badly.
- How do you keep AI output safe and reliable?
- Output is measured against an agreed bar before launch and monitored for drift after it, with human review wherever the stakes justify it.
- How much does AI automation cost for a UK business?
- The workflow assessment is a fixed fee; a first production automation typically starts from £8,000 as a fixed-scope project. Because we start with one measurable workflow, the spend is staged - each step has to pay for itself in the numbers before the next is priced.
- How long before something is live?
- For a well-chosen first workflow, weeks - our fastest from engagement to live-behind-a-flag is five. The pace comes from scoping, not corner-cutting: one measurable workflow, graded before it is built.
- What happens to our data - and what about UK GDPR?
- Data protection is a design input here, not a compliance afterthought. We work within your residency and privacy requirements, choose providers accordingly - including self-hosted models where data cannot leave - and put the data-processing questions on the table before the build, not after.
- Do we need our data sorted out first?
- Not always - plenty of automation runs on the documents and inboxes you already have. Where a workflow does depend on clean, structured data, we will say so plainly, and that foundation can be sequenced first; it is what our data engineering service exists for.
- Will this replace members of our team?
- What it reliably does is take the repetitive share of a queue, so the same people handle the exceptions, the judgement calls and the backlog nobody was reaching. What that means for headcount is your business decision; ours is to make sure everything important still reaches a human.
- Do you build customer-facing chatbots?
- When the workflow justifies one, yes - scoped to what it can be trusted with and honest about handing off to a person. What we will not build is the chatbot that promises to answer anything, because “anything” has no pass mark and its failures land on your customers.
- We already tried an AI pilot and it went nowhere. Is that a bad sign?
- It is the most common starting point we see, and usually the diagnosis is the same: the pilot was scoped to impress rather than to be measured. The fix is not a better model - it is a workflow with a pass mark. That is where our assessment starts.
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