AI & Automation
Automate the work that is eating your team’s week
Most of the value available from AI in a UK SME is not a chatbot. It is the four hours a week someone spends copying figures from PDF invoices into a spreadsheet, or reading the shared inbox and forwarding things to the right person. That work is well-defined, high volume, and genuinely suited to automation.
Indicative
Assessment £3,000–£6,000; build from £8,000
Fixed price agreed in writing before any build starts.
Get a quote+44 7488 265083The problem this solves
The reason these projects fail is rarely the model. It is that nobody measured the task first. Automating a process that happens twice a month saves nothing; automating one where a mistake costs a customer relationship needs a human check, which changes the business case entirely. Both are knowable before you build.
What you get
Task audit with real numbers
Frequency, time per instance, cost of an error, and how consistent the inputs are. Some tasks come out of this clearly worth automating and some clearly not — you get both answers.
Document extraction that reports confidence
Invoices, purchase orders, delivery notes and forms parsed into structured data, with a confidence score per field. Low-confidence extractions route to a person rather than silently guessing.
Email and enquiry triage
Incoming messages classified, routed and pre-drafted for approval. Approval rather than auto-send, because the failure mode of an automated reply to an angry customer is expensive.
Human in the loop by design
A review queue for anything below the confidence threshold, which starts high and comes down as the accuracy data supports it. Nobody has to trust it on day one.
Integration with the systems you already use
Results written into your accounting package, CRM or database — not into another dashboard someone has to check. An automation that ends in a new place to look has not saved anything.
Accuracy monitoring
Per-field accuracy tracked against the human corrections, so you can see whether it is getting better and where it consistently fails.
How we work
Shadow the process
Watch the task being done and time it. This consistently finds steps nobody mentioned in the briefing.
Business case
Hours saved against build and running cost, with a payback period. If it does not pay back inside a year we will tell you.
Benchmark on your data
Run candidate approaches against a sample of your real documents and measure accuracy before building anything. Vendor accuracy claims mean nothing on your inputs.
Build with the review queue first
The human check exists from day one, not added after the first bad extraction.
Parallel run
Automation and manual process run side by side for a fortnight, differences reviewed daily. This is how trust gets earned.
Cut over and tune
Thresholds relaxed gradually as the accuracy data justifies it.
What you should expect
- Hours per week returned to the team, measured rather than estimated
- Errors routed to a person instead of propagating silently
- Results landing in the systems you already work in
- A documented accuracy figure you can point at
Built with
- Mistral
- OpenAI
- Anthropic Claude
- Python
- TypeScript
- Node.js
- PostgreSQL
- Redis
- RabbitMQ
- Tesseract
- AWS Textract
- n8n
- Zapier
- Xero
- QuickBooks
Mainstream, well-supported technology — chosen so you can hire for it and so another team could take the project over.
AI Process Automation — your questions
Including the ones about cost, which most agencies leave off the page.
A feasibility assessment with benchmarking on your own data is £3,000 to £6,000, and you get the numbers whether or not you proceed. A single automated workflow typically runs £8,000 to £20,000 to build, plus model and hosting costs that are usually tens of pounds a month rather than hundreds.
On structured documents like invoices from known suppliers, high — and we will give you a measured figure on your own sample rather than a claim. On messy, inconsistent inputs it is lower, which is exactly why the review queue exists. Anyone quoting an accuracy percentage before seeing your documents is guessing.
When the rule is deterministic — a regular expression or a lookup table is cheaper, faster and does not hallucinate. When volume is low, because the build cost never pays back. And when an error is unrecoverable and cannot be caught by review. We turn down work on all three grounds.
Not on the API tiers we use, and we confirm the specific terms in writing for whichever provider your project ends up on. Where data cannot leave your infrastructure at all, we can run open-weight models on your own hardware — with an honest note that quality is somewhat below the frontier hosted models.
In practice it removes the part of a role nobody wanted to do. We are straight about it because the projects that succeed are the ones where the team helped design the automation, and the ones that fail are where people quietly worked around it.
The integration is written behind an adapter, so switching provider or model is a configuration change and a re-run of the benchmark rather than a rebuild. Given how fast this field moves, anything else would be negligent.
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Read moreTalk to someone who has built this before
A short call is usually enough to tell you whether this is the right service for your situation — including when it is not.