AI & ML

Machine learning that runs reliably in production.

Predictive models, forecasting, and anomaly detection, with the engineering to keep them accurate after launch.

Start a conversation Accelerators we bring
The problem

Models work in a notebook and fail in production. Input data drifts. Retraining is manual. Monitoring is missing. Nobody can explain to a regulator or a customer why the model made a decision. The algorithm is rarely the problem. The engineering and data around it are.

What we do

We build models on governed data with automated feature pipelines. We set up MLOps for training, deployment, monitoring, and retraining. We document how each model works and what its limits are. We prefer the simplest model that solves the problem.

What we deliver

What you get.

The outcome

Models that stay accurate and that you can explain.

  • Predictive modeling, forecasting, and optimization
  • Anomaly detection and risk scoring
  • MLOps: feature stores, CI/CD for models, monitoring, and retraining
  • Model explainability, validation, and documentation
  • Production hardening of existing models
When to call us

Signs this is the right service.

  • Models work in notebooks but not in production.
  • Nobody can say whether a production model is still accurate.
  • Retraining is manual and happens when someone remembers.
  • A regulator, customer, or auditor has asked how a model makes its decisions.
Accelerators

What we bring to this work.

Templates, code, tests, and documents we adapt to your data and systems, so the work starts on your problem. What we adapt for you is yours to keep.

MLOps Templates

Pipelines to deploy, monitor, and retrain machine learning models.

  • Training and deployment pipelines with evaluation checks
  • Feature pipeline and feature store patterns
  • Monitoring for drift, data freshness, accuracy, and cost
  • Evaluation Card and model documentation templates
IterateShipTrustLearn More

Data Readiness Assessment

Check whether your data can support a decision before you fund the work.

  • Profiling and lineage review of the sources in scope
  • A draft Decision Brief for the use case
  • A list of gaps, ranked by effort and by effect on the decision
  • A plan for the first build increment
GroundLearn More
How it starts

The first step.

We take one model, new or existing, and put it on a release path: training from code, tests against agreed thresholds, approval by someone other than the builder, monitoring for drift and accuracy, and a tested rollback. Then the next model uses the same path.

Every engagement runs on GIST: Ground, Iterate, Ship, Trust.

How we work

See what the first step would be.

Tell us the business decision you want to improve. We will tell you what the first step would be and whether we are the right fit.

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