Technology & engineering

Artificial Intelligence

We start from a process that costs you time, not from a model. If AI is the right tool, we build it, measure it and keep it under control. If it is not, we say so.

Close-up of a processor on a circuit board
What is included

Four parts to every artificial intelligence engagement

01

Process assessment

Find the tasks where automation pays back, and the ones where it will not.

02

Assistants and search

Answers drawn from your own documents, with sources cited and permissions respected.

03

Workflow automation

Classify, extract and route documents, tickets and email, with a person only for the unusual cases.

04

Evaluation and guardrails

Test sets, accuracy tracking and human review, before and after launch.

What you get

Deliverables, written down before we start

These are agreed in the scope for each stage. If one is missing at the end, the stage is not finished.

  • Use-case assessment with expected effort and return
  • Working pilot measured against an agreed test set
  • Data handling and security notes
  • Monitoring for accuracy and cost after launch
Platforms and tools

What we use day to day

We work with what you already run where it makes sense, and explain any recommendation to change.

  • Python
  • Azure OpenAI
  • LangChain
  • PyTorch
  • FastAPI
  • Vector databases
Ask about your stack
How we work

Four stages, no surprises

01

Discovery

Interviews, system access and a review of what exists. Usually one to two weeks, with written findings.

02

Strategy

Options, costs and a recommendation. You choose the scope for the next stage, and nothing more.

03

Delivery

The agreed work in short cycles, demonstrated as it goes, with tests and documentation included.

04

Operation

Ongoing support under clear response times, or a full handover to your team.

Tell us what needs fixing

A short description is enough. We reply with questions, not a sales deck.