Predictive models built around the decision they need to improve.
We build machine learning models for forecasting, risk scoring, anomaly detection and recommendations — starting from the decision a team makes today, defining the data it needs, and measuring the model against a baseline you already understand.
- Problem framing and baseline
- Data audit and feature pipeline
- Trained and validated model
- Explanation and reporting
- Scoring service or batch job
- Monitoring plan
Where this helps
What we deliver
How it works
- 01
Frame the decision
We work with the team that owns the decision to define the target, the timing and what a useful prediction looks like.
- 02
Assess the data
History, labels and leakage risks are reviewed. If the data cannot support the goal, we say so before modeling starts.
- 03
Model and compare
Simple baselines first, then more complex methods only when they earn their added complexity.
- 04
Validate with the business
Results are reviewed segment by segment with the people who will act on them, including cases the model gets wrong.
- 05
Deploy and monitor
The model goes into the workflow, with monitoring and a retraining schedule agreed before launch.
Design decisions we make with you
Interpretability versus accuracy
In regulated or high-stakes decisions, a slightly less accurate but explainable model is often the right choice.
Batch or real time
Nightly scores in a warehouse are cheaper and simpler; real-time scoring is used only where the decision cannot wait.
Data foundations
Models depend on reliable pipelines. When the data layer needs work, our [data engineering](/automation-data/data-engineering) team handles it.
Human in the loop
Which predictions trigger action automatically and which go to a person to review.
Retraining cadence
How often the model is refreshed, based on how fast your data changes, and who approves a new version.
Applications
Related capabilities
- Demand ForecastingForecast demand by product, location and week, with the uncertainty visible, so planners can make and explain their decisions.
- Fraud & Risk SignalsScore transactions, claims or applications for risk and send the suspicious ones to investigators with the reasons attached.
- Predictive MaintenanceUse equipment signals and maintenance history to rank which assets need attention, so maintenance teams can plan work before failures.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Computer VisionImage and video models for inspection, counting, monitoring and visual document work, built for the conditions they will run in.
- AI Consulting & StrategyFind the AI opportunities worth funding, test whether your data and systems can support them, and leave with a sequenced roadmap.
Questions buyers ask
It depends on the problem and how often the pattern repeats. We assess your history and labels in the first phase and tell you plainly whether they can support a useful model.
No. If a few clear rules capture the logic, keep them. ML earns its place when patterns are too many or too subtle to write down, and we compare against the rule-based baseline.
Yes. We provide feature-level explanations for individual predictions and document known limits, which matters for regulated decisions and for user trust.
We set up monitoring and retraining and can operate it for you, or hand over to your team with runbooks. See MLOps & LLMOps.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.