Practical AI
AI & machine learning
Models that run on Monday morning.
Predictive models, NLP and automation — built on clean data, with an owner, and a way to tell when they are wrong.
The usual problem
The graveyard of AI projects is full of notebooks that never left a laptop. The model was clever. Nobody owned the data it needed, and nobody watched it after week three.
How we approach it
We treat models as production systems. That means data readiness first, a narrow question, a baseline a human could beat, and monitoring once it is live. If it does not change a decision, we do not ship it.
What good looks like
- A model with a named owner and a runbook
- Predictions tied to a decision, not a dashboard nobody opens
- A clear no when the data is not ready — before you spend the budget
Typical work
- AI strategy: what is worth automating, and what is theatre
- Data cleaning, feature stores and training pipelines
- Custom models for forecasting, classification and document work
- NLP and computer vision where the use case is real
- MLOps: drift, accuracy, rollback, and a human in the loop
Tell us the problem. We’ll tell you if we can help.
A short conversation is usually enough to know whether this is a two-week discovery or a longer piece of work. No pitch deck.