AI applied to one problem you can measure, not a strategy deck
Practical machine learning and AI where there is a real case for it — document handling, forecasting, classification, internal assistants. We will tell you when the honest answer is a database query and a rule.
Right for you if
- You have a repetitive judgement task consuming a lot of staff time
- You hold years of operational data and have never used it to predict anything
- You have been quoted for an AI project and want a second opinion on whether it is real
What we do
- Assess the use case honestly, including whether AI is the wrong tool for it
- Check data readiness — volume, labelling, quality and legal basis for use
- Build the model or automation, or integrate an existing foundation model
- Design the human review step, because a confident wrong answer is the main risk
- Evaluate accuracy against a measure agreed before the build starts
- Deploy, monitor for drift, and maintain
How we work
What working with us actually looks like
Use-case review
A short engagement ending in a recommendation, including "do not build this" where that is the answer.
Data readiness
We check what you hold, how clean it is, and whether you may lawfully use it this way.
Pilot
One bounded problem, one measurable success criterion, agreed up front.
Human review
Design the checkpoint where a person confirms or overrides. Non-negotiable for anything customer-facing.
Deploy and monitor
Accuracy tracked over time. Models decay, and nobody notices unless someone is watching.
Technologies
What we build it with
- Python
- scikit-learn
- PyTorch
- OpenAI & Anthropic APIs
- Retrieval-augmented generation
- PostgreSQL & pgvector
- AWS SageMaker
- Azure AI
Start a conversation
Talk to us about ml & ai applications
Thirty minutes with an engineer who has done this before. We’ll tell you what we’d do, roughly what it costs, and whether we’re the right fit.