01 · Custom AI systems
AI designed around one job, done well.
Off-the-shelf AI tools are built for everyone. A custom system is built for a specific process in your organisation — the data it needs, the decision it supports, the people who use it and the controls around it.
Where it fits
When a custom system beats a subscription.
Custom makes sense when the process is specific to you, the data lives in your systems, and the result has to be explainable to someone accountable for it.
- Triage and prioritisation — claims, tickets, applications or referrals sorted by urgency and type, with a rationale for each.
- Review and compliance checks — documents compared against policies, contracts or standards, with exceptions highlighted.
- Recommendations — next best action for staff, grounded in history and rules rather than guesswork.
- Forecasting and anomaly detection — classic machine learning where it’s the better tool, combined with LLMs where language is involved.
- Decision support — summarising a case from several systems so a person can decide faster, with the sources one click away.
How we build it
From process map to monitored system.
Map the decision
Who decides what today, with which information, and what a good outcome looks like. This becomes the success measure.
Prove it on real data
A focused prototype on a representative sample, scored against the success measure before anything else is built.
Engineer the system
Pipelines, integrations, interfaces, guardrails, logging and an evaluation harness — the parts that make it dependable.
Launch and tune
A staged rollout, monitoring of quality and cost, and a backlog of improvements driven by real usage.
Timeframes depend on the size of the build; most small to medium builds take 8 to 10 weeks.
What you get
A system you can own and explain.
Working software
Deployed into your environment or ours, integrated with the systems the process depends on.
Evaluation suite
The test set and scoring used to accept the system, so every future change can be measured the same way.
Documentation
Architecture, data flows, model choices, known limitations and runbooks — written for the people who’ll be accountable.
FAQ
Custom AI questions.
Do we need a data science team to run it?
No. We build systems to be operated by your existing technical team, with monitoring, runbooks and an evaluation suite. We can also support the system after launch.
What if our data isn’t ready?
That’s common. Scoping includes a data review; sometimes the first deliverable is a pipeline that cleans and joins the data the AI will need.
Is it always an LLM?
No. We use large language models where language is involved and conventional machine learning or plain rules where they’re more accurate, cheaper or easier to explain.
Got a process that’s begging for AI?
Describe the decision, the data and who’s involved. We’ll tell you honestly whether a custom system is worth it.