Service
AI-enabled software
Practical use of machine learning and language models inside business software: document extraction, classification, assistants over your own data, and predictions from your operational history.
Where AI earns its place
- Documents: extracting fields from invoices, delivery notes, forms and scans, then validating them against your records before anything is posted.
- Classification and routing: sorting incoming messages, tickets, leads or defects so the right person sees them first.
- Assistants over your own data: answering questions from your manuals, records and history, with citations, and only for data the user is allowed to see.
- Prediction: demand, maintenance windows or risk scores from your operational history, where there is enough data to justify it.
How we keep it dependable
AI components are treated like any other untrusted input. Model output is validated, logged and, where it matters, confirmed by a person before it changes a record. Assistants reach data only through the application's own authorised services, so a user cannot ask the model for something they could not see in the system. We measure accuracy on your data before and after launch, and we design the workflow so a wrong answer is cheap to catch.
Where a plain rule or a small model does the job, we use that instead of a large language model. It is cheaper, faster, and easier to explain.
Delivery
AI features are delivered inside working software: a web system, an automation, an integration. You receive the same handover as any other project, including how the model is called, what it costs to run, and how to switch providers or models later without rebuilding.
Questions we are often asked
Does our data get sent to third parties?
Only if the design says so, and you decide. We can run models locally or on your own cloud account; where a hosted model is the right choice, we document exactly what is sent and under what terms.
Do we need a lot of data?
Document extraction and assistants work from day one with current models. Prediction needs history; we will tell you honestly whether you have enough.