AI engineering
Machine learning inside real workflows — not demos, and not for its own sake.
The useful applications of AI in most businesses are unglamorous: reading documents that arrive as PDFs, classifying incoming requests, drafting text a person then edits, finding the right record in a system nobody can search properly.
We build those, and we are candid about the cases where a rules engine would be cheaper, more predictable and easier to defend to an auditor. Quite often it would be.
What we build
LLM integration
Language models embedded in real workflows with evaluation, guardrails and sensible fallbacks when the model is wrong — because it sometimes will be.
Document and data extraction
Turning invoices, contracts and forms into structured data, with confidence scoring and human review where accuracy matters.
Retrieval systems
Search over your own documents and data, so answers cite a source your team can verify.
Process automation
Classification, routing and triage for high-volume repetitive decisions, with an audit trail.
What you get
- Evaluation set built from your real data, not synthetic examples
- Measured accuracy on that set before anything goes live
- Human-in-the-loop review for decisions that carry risk
- Cost-per-operation modelled so running cost is predictable
- A documented fallback for when the model is unavailable or wrong
Questions about ai engineering
How do we know the AI is accurate enough?
We build an evaluation set from your real data first and measure against it, so accuracy is a number you have seen rather than a claim. If it does not clear the bar for your use case, we say so before you spend on a full build.
Will our data be used to train models?
Not without your explicit instruction. We use enterprise API tiers that exclude data from training by default, and for sensitive workloads we can architect around models you host yourself.
Is AI the right answer for our problem?
Sometimes not, and we will tell you. If the decision rules are stable and writable, a conventional system is cheaper to run, easier to audit and does not drift. We would rather build you the correct thing than the fashionable one.
What does it cost to run?
It depends on volume and model choice, and it is a real ongoing cost rather than a one-off. We model cost per operation during discovery so you can see the monthly figure before committing.
Related services
Tell us what you're building
Start with a sentence about the problem. We reply within one business day, with honest questions and — if we're the right fit — a proposal with a fixed number on it.