Put AI to work inside the systems you already run
We build machine learning and AI features on your own data: prediction, classification, vision and voice. Wired into your production systems with the Claude API and local models, not left sitting in a notebook.
- Trained on your own data
- Deployed behind production APIs
- Claude API and local models
AI work, in real numbers
Six ways we put machine learning to work
We pick the technique that fits the problem: prediction, vision, language or scheduling. One model rarely does everything well.
Predictive analytics
Forecast demand, churn and equipment failures from your own history, so you act on a signal instead of a surprise.
Computer vision
Image recognition and visual inspection that reads photos and camera feeds, and does not get tired on the night shift.
Language and classifiers
Reply classifiers, document parsing and text sorting. We already run classifiers that route thousands of emails a day.
Recommendation systems
Product and next-step suggestions driven by what people actually did, not a rule someone hand-wrote.
Anomaly detection
Flag fraud, defects and odd behaviour the moment a value drifts from the pattern the model learned.
Process optimization
Scheduling, routing and resource allocation, driven by data, that cut wasted time out of work you already do.
The jobs clients ask for first
These are the applications clients put into production first, each one tied to a number they can watch move.
- Churn prediction with retention triggers
- Demand forecasts for inventory and staffing
- Image recognition and visual inspection
- A voice assistant that covers 2,210 industries
- Reply classifiers that route incoming email
Built for data-heavy, decision-heavy sectors
A production-grade ML stack
Modelling frameworks in Python, served through the same Go and FastAPI backends that run our live products.
How we deliver AI projects
A measured path from data audit to a monitored model in production.
Data audit
We check your data sources, quality and volume to confirm ML is the right tool, and where it pays back first.
Model design
Prototype candidate models against a measurable baseline so accuracy targets are proven before the full build.
Train and validate
Train, evaluate and stress models against real production data with clear, reviewable metrics.
Deploy and monitor
Ship behind an API with monitoring, drift detection and a retraining loop that keeps accuracy from decaying.
Ready to put AI to work?
Tell us the decision you want to automate or the metric you want to predict, and we'll scope the model, the data and the cost.