Classic machine learning
Regression, classification and gradient-boosted trees for structured data.
A forecast you can act on, with its error range attached.
We offer classic machine learning and deep learning for the questions your business keeps guessing at: demand, churn, failures, risk. Each model is checked against history it never saw, then delivered into the tool where the decision is made.
Next quarter, with its error range. Checked against periods the model never saw.
From your history to the decision
A model only pays off inside the system where someone acts on it, so that is where we connect it.
ML
History
Features
Model
Validation
Your ERP
Monitoring
What this fixes
- Inventory, staffing or budget set from gut feel or a trailing average.
- Churn, fraud or failures noticed after they happen.
- A notebook model that one analyst understands and nobody runs.
- Forecasts that break whenever a promotion or a shock changes the pattern.
What we offer
Deep learning
Neural models for text, images, sequences and other unstructured data, when simpler methods fall short.
Validation
Backtests on held-out periods, and plain statements of where the model will be wrong.
Production
Models on a schedule or behind an API, with drift monitoring and retraining.
What your team gets
A prediction with a known error range, inside the tool your team already uses, and the pipeline to keep it accurate as conditions change.
Where AI comes in
This is where we offer classic machine learning and deep learning, and where they connect to AI & Automation: a forecast or a classifier can be one of the tools an agent uses. See AI & Automation
When this isn’t the right fit
If a trailing average explains most of the pattern, or you have little consistent history, a model adds cost without adding accuracy. Start with the data work.
Which decision are you still guessing at?
Tell us the decision and the data behind it. We’ll say whether a model can beat what you do today.
Bring us the decision you’re guessing at