Documents processed by hand
Invoices, forms, contracts and reports read and re-keyed by people.
Somebody spends hours each week on work a machine should be doing.
AI and automation that solve real business problems
Applied to a specific, measurable task — not a demonstration that impresses in a meeting and never reaches production. We are equally willing to tell you when a problem does not need AI at all.
Problems we solve
Where this usually starts.
Knowledge nobody can find
Answers that exist somewhere in your files, wiki or ticket history, and take an hour to locate.
Repetitive decisions
Routing, triage and classification done manually against consistent rules.
Support load
The same questions answered repeatedly, from information already written down.
What we build
Typical solutions.
- AI assistants and internal copilots
- RAG systems over your own documents
- Document extraction and processing
- Internal knowledge search
- Workflow and approval automation
- Classification and routing
What is included
Every engagement covers.
- Discovery and requirements
- UX and interface design
- Architecture and data modelling
- Development and code review
- API design and integration
- Testing and QA
- Deployment and production setup
- Documentation
- Handover of code and accounts
Technology we commonly use
- Claude
- OpenAI
- Python
- TypeScript
- Vector databases
- LangChain
Chosen per project against the requirement — not a fixed stack, and not a list of everything we have ever touched.
Questions
The things people actually ask.
Will our data be used to train someone elses model?
Not with the configurations we use. Enterprise API tiers from the major providers exclude your inputs from training by default, and we can keep processing within a region you specify. If your requirements rule out third-party providers entirely, self-hosted models are an option with different cost and capability trade-offs.
How accurate is it?
That depends on the task, and it is the right question to ask. Extraction from structured documents is highly reliable; open-ended judgement is not. We define what accuracy has to be before building, measure it, and design a human review step where the number does not justify full automation.
Is AI the right answer for our problem?
Often not. A great deal of what gets pitched as AI is a rules engine or a decent integration wearing a costume, and those are cheaper to build and far easier to maintain. We will say so.
What does it cost to run?
Model usage is billed per token, so running cost scales with volume rather than sitting flat. We estimate it during scoping and design around it — caching, smaller models for simple steps — rather than presenting a surprise later.
Get in touch
Tell us what you’re trying to build.
You don’t need a finished specification. Describe the problem, the existing process or the product idea, and we’ll take it from there.