We build AI assistants and automations that take real work off your team. Customer support that answers itself. Reports that write themselves. Workflows that just run, quietly, in the background.
Not demos. Real production systems with monitoring and support. We tune them as your business grows, and you only see them when something needs your attention.
Answer questions, qualify leads, book calls
The repetitive stuff, automated
Finance, HR, support, ops
Leadership dashboards that update themselves
Based on your docs, not made up
Monitored, improved, supported
AI automation is the practice of handing a repeatable business process to a system that can read unstructured input, decide what to do with it, and act, without a person driving each step. It is not a chatbot bolted onto a website, and it is not a single prompt saved in a document. It is a production system with inputs, rules, escalation paths, logging, and an owner.
Most of the work we ship falls into three shapes. The first is intake: something arrives, an email, a form, a document, a call transcript, and the system classifies it, extracts the fields that matter, and routes it to the right queue. The second is throughput: a process that already exists but runs on copy and paste, reconciliation, data entry, report assembly, and the system does the mechanical part while a person approves the outcome. The third is retrieval: a question gets asked against your own documents and the system answers from them rather than from the open internet.
The distinction that matters commercially is between an automation that saves a person twenty minutes and one that removes a headcount-level constraint. We scope for the second. If a process does not run often enough, or does not cost enough when it goes wrong, automating it will not pay for the engineering.
Every engagement starts with a process map, not a tool choice. We sit with the people who currently do the work and write down what actually happens, including the exceptions they handle without thinking about them. The exceptions are usually where the project succeeds or fails. A system that handles the happy path and dumps everything else on a human has not removed the work, it has moved it.
We then fix the boundary. Which decisions the system makes on its own, which it proposes for approval, and which it never touches. That boundary is a compliance artefact as much as an engineering one, and in regulated sectors it is the thing an auditor will ask to see. Anything with a legal or material effect on a person stays behind human approval by default.
Build runs against a fixed spec with a fixed price. We integrate with the systems you already run rather than asking you to move, and we instrument everything so that when a workflow drifts you find out from a dashboard rather than from a customer. After go-live we stay on the system, because an automation that nobody tunes degrades as the business around it changes.
The common failure is not technical. It is that the automation was built for a process nobody had agreed on. Two departments describe the same workflow differently, the system is built to one description, and the other department stops using it within a month. Writing the process down and getting it signed off is unglamorous and it is the highest-leverage hour in the project.
The second failure is scope without a measurement. If nobody agreed what the system was supposed to reduce, hours, error rate, turnaround time, then there is no way to say whether it worked, and no basis for the next investment. We fix a baseline before build starts and report against it after.
The third is treating the model as the product. Models change, get cheaper, and get replaced. The durable asset is the process definition, the integrations, the evaluation set, and the audit trail around it. Build those properly and swapping the model underneath is a maintenance task rather than a rewrite.
The workflows below are the ones that recur across engagements in each sector, alongside the compliance framework that governs them.
Clinical admin, patient flow, and compliance automation.
UK · UK GDPR Articles 22A to 22D, NHS DSPT, CQC regulations, and ICO guidance on automated processing in clinical settings. Health data is special category data, so solely automated decisions with legal effect on patients stay restricted and require human-in-the-loop controls.
KYC, AML, reconciliation, and regulatory reporting.
UK · FCA PS22/9, UK GDPR Articles 22A to 22D, FCA model risk management guidance, and PRA supervisory statements on algorithmic systems used in regulated activities.
Document review, client intake, and billing automation.
UK · SRA Code of Conduct 2011, UK GDPR, and SRA Technology and Innovation guidance govern legal AI deployments. Matter data must remain within approved jurisdictions.
Predictive maintenance, quality control, and supply chain.
UK · UK GDPR, Health and Safety at Work Act 1974, and ISO 9001:2015 quality management requirements apply to manufacturing AI systems.
Admissions, student support, and administrative automation.
UK · UK GDPR, DPA 2018, Ofsted regulatory framework, and JISC guidance on AI in higher education apply to student data processing.
Inventory, pricing, fulfilment, and customer service automation.
UK · UK GDPR, Consumer Rights Act 2015, FCA BNPL regulations, and Payment Services Regulations 2017 apply to e-commerce AI systems.
Working from a specific city? See AI automation by location.
Every project is fixed-price against agreed deliverables. Running costs after go-live depend on volume and are quoted separately. Not sure which of these you need? Start with the free scan, and the fee comes off the build.
| Scope | United Kingdom | United States | Timeline |
|---|---|---|---|
| Single workflowOne process, end to end, with the integrations it needs and a compliance review before go-live. | £8,000–£25,000 | $10,000–$35,000 | 6–10 weeks |
| Multi-workflow buildA connected set of processes sharing data, routing, and audit logging across more than one team. | £30,000–£80,000 | $40,000–$100,000 | 12–20 weeks |
| AEO AuditStarts from a free AEO scan of your site, then covers every failing check and what it costs, the fixes in payback order, what answer engines currently say about you, and the automatable share of the process behind it. Yours to keep whether or not we build. | On enquiry | On enquiry | 5 working days |
You tell us what you are trying to build and why. We tell you, plainly, whether we are the right team to do it.
Week 0 · FreeA short, focused audit of where you are today and a clear plan for what to build next. Yours to keep, even if we do not work together.
5 working days · On enquiryOnce we both agree, we get going. Clear scope, agreed goals, and a single point of contact on our side.
Week 4 · Project startsWeekly progress, monthly check-ins, quarterly reviews. We build it, ship it, and keep it running after launch.
Month 2 onward