AI & Machine Learning
Models that reach production, on data you can defend.
We approach machine learning from the data side first. Most organisations asking for models actually need the layer underneath fixed — and we will tell you that rather than build something impressive on unreliable inputs.
What we do
- Feature pipelines & feature storesReproducible feature engineering with consistent definitions between training and serving.
- Model training workflowsVersioned, reproducible training with experiment tracking and a registry that records what shipped and why.
- MLOps & deploymentDeployment pipelines, rollback paths, monitoring for drift and degradation, and retraining schedules.
- Retrieval-augmented generationDocument ingestion, chunking, embedding and retrieval over your own corpus, with evaluation and citation of sources.
- AI governance & model riskModel documentation, lineage from training data to prediction, bias assessment, and the record a regulator will eventually ask for.
Technologies
Pythonscikit-learnXGBoostMLflowAmazon SageMakerAmazon BedrockVector storesAirflow
Who usually owns the budget
CTO, Head of Data Science, or a product owner with a use case and no path to production.
What triggers the purchase
- Models work in notebooks and never ship
- A deployed model has quietly degraded and nobody noticed
- An AI initiative was announced before the data layer was ready
Is this the piece you need?
Twenty minutes on a call is usually enough to know whether this is the right starting point.