AI & Automations
Predictive Analytics & Machine Learning
Forecasts and models built on the data you already hold
Most businesses already hold the history a forecast needs: years of orders, visits, tickets or readings. What they lack is a model built on it that someone trusts enough to act on. We build predictive models that start from a plain question, are measured against a simple baseline, and keep running after the project ends.
- Demand and sales forecasting
- Churn and risk scoring
- Anomaly detection
- Feature engineering
- Model evaluation
- MLOps and monitoring
Why it matters
A model is only useful if the people it is built for believe it. That means an honest baseline, clear limits, and output shaped around a decision somebody actually has to make.
What this includes
Six parts of the work. Which of them you need depends on where you are starting from, and we agree that before anything is built.
- 01
Demand and sales forecasting
Forecasts at the level you plan at, whether that is product, branch or week, with the uncertainty shown alongside the number.
- 02
Churn and risk scoring
Scores that rank which customers, accounts or assets need attention first, with the reasons behind each score visible.
- 03
Anomaly detection
Models that flag unusual transactions, readings or behavior early, tuned so the alerts are worth reading.
- 04
Feature engineering
Turning raw operational data into the signals a model can learn from, documented so they can be rebuilt.
- 05
Model evaluation
Every model compared against a simple baseline on data it has never seen, before anyone relies on it.
- 06
MLOps and monitoring
Scheduled retraining, versioned models and drift checks, so a model does not quietly degrade after launch.
Not sure which of these you need?
Tell us where things stand for you today and we will suggest where to start.
How we deliver it
Four stages, agreed with you before the work starts, with a working result at the end of each rather than one reveal at the end.
Frame the decision
We agree on the decision the model supports, how success is measured, and what a wrong answer costs.
Prepare the data
We assemble, clean and explore the history, and confirm there is enough signal to be worth modeling.
Build and compare
We start with the simplest workable approach and add complexity only when it earns its place against the baseline.
Deploy and monitor
The model goes into a scheduled pipeline with monitoring, documentation and a handover to your team.
The stack
Technologies we work with
The tools we reach for most often in this work. We fit in with what you already run rather than asking you to change it.
3 groups
- 01 / 035
Modeling
- Python
- scikit-learn
- XGBoost
- PyTorch
- statsmodels
- 02 / 034
Platforms
- Snowflake
- Amazon SageMaker
- Azure Machine Learning
- Databricks
- 03 / 033
Operations
- MLflow
- Apache Airflow
- Docker
What you receive
Yours to keep when the engagement ends, whether we carry on working together or not.
A documented model
How it works, what data it uses, how well it performs and where it should not be trusted.
A scheduled pipeline
The model runs on new data automatically, with its inputs and outputs recorded.
Monitoring and alerts
Checks for data drift and falling accuracy, with a clear owner for each alert.
A handover session
Your team walks through retraining and troubleshooting before the engagement closes.
Common questions
The ones that come up in almost every first conversation about this work.
It depends on the question. A few years of consistent history is often enough for forecasting, and an early data review tells you quickly whether a model is realistic before you invest in building one.
Yes. We favor models that can be explained, show which factors drive each prediction, and document the limits alongside the results.
The model is scheduled, monitored and retrained on new data. Your team receives the code, the documentation and a walkthrough, so it does not depend on us to keep running.
Let's talk about Predictive Analytics & ML
A short call is usually enough to work out whether we can help and where it makes sense to start. No preparation needed.