AI Development
AI-powered applications and machine learning that automate real processes and surface insight your team can act on — not demos that impress once and then sit unused.
What we build
We start from the process you want changed, not from the model. If a rules engine or a better form would solve it, we will tell you that instead.
Assistants, document understanding, search over your own content and drafting tools — wired into the systems your team already uses, with guardrails and human review where the output matters.
The repetitive middle of a process — triage, extraction, classification, routing, summarising — handled automatically, with exceptions escalated to a person.
Models trained on your own data to forecast demand, flag risk and spot anomalies, delivered with the pipelines and monitoring that keep them honest in production.
Why this works
AI is embedded in every phase of our own delivery lifecycle, so the practices we put into your product are the ones we rely on daily.
Retrieval over your own documents and systems, so answers cite something real instead of being invented by a model.
Every automation ships with a baseline and a measure. If it is not beating the manual process, we change it or stop it.
The further a decision reaches, the more review it gets. People stay in the loop wherever an error would be expensive.
How it runs
We look at your processes and data and come back with a short list of candidates ranked by value and feasibility — including the ones not worth doing.
One candidate, built small and tested against real data and a real baseline, in weeks rather than quarters.
The working version: pipelines, guardrails, evaluation, logging and the integration into the systems your team actually uses.
Accuracy, cost per task and time saved, measured against the baseline we agreed at the start.
Monitoring for drift and anomalies, prompt and model updates as the landscape moves, and a regular review of what to automate next.
Coverage
Questions
With a process that is repetitive, high volume and currently done by people reading text. Those are the ones where the payback is quickest and the risk is lowest. Our opportunity review ranks your candidates and says which to leave alone.
No. We use business accounts with training disabled, and where your data is sensitive we keep it inside your own cloud tenancy. The Security & Compliance page sets out how we handle data and AI tooling.
By grounding answers in your own content, constraining the output format, evaluating against a test set before release, and keeping a person in the loop for anything consequential. We also show users where an answer came from.
Usually a few weeks for a single, well-chosen use case against real data. The point of the proof of concept is to find out whether it works before you commit to a production build.
That depends on volume and the model you end up using, which is why we track cost per task from the proof of concept onward and design for the cheapest model that passes your quality bar.
AI Development
That is the normal position. Tell us where the manual work piles up and we will come back with the two or three places AI is worth the effort — and the ones that are not.
Other services
Quick answers about our services