Predictive analytics,
models that ship.
Forecasting, churn, propensity, uplift and pricing models, engineered against the decisions they inform, not delivered as slides.
A model that lives in a notebook is a report. A model that lives in a decision workflow is a product. We build the latter, with the boring engineering that makes it possible.
Frame the decision
Which decision, at what cadence, with what tolerance for wrong? The model follows.
We start from the decision workflow, who acts on the output, when, with what latitude, and shape the model against it. A great model that lands in the wrong workflow is a wasted quarter.
Sometimes framing kills a model. That's the highest-return work we could have done.
A model that lives in a notebook is a report. A model that lives in a workflow is a product.
Build honestly
Feature engineering, model selection and validation against the actual data, with baselines that keep us honest.
Simpler models beaten by complex ones only when it matters. Cross-validation that reflects the deployment environment. Baselines and stress-tests documented alongside the model.
The output is not a leaderboard entry. It's a model your team can deploy and re-train.
Ship it
Serving, monitoring, drift detection and retraining pipelines, engineered as first-class platform.
Model registry, feature store, deployment automation, monitoring and drift detection. Rollback pathways are as important as roll-forward.
Serving latency and cost are engineered against real numbers, not against benchmarks.
Operate & improve
Feedback loops, retraining and evaluation as ongoing engineering, because the world drifts, and models must too.
Ground truth captured, retraining on a real cadence, champion/challenger deployments, and honest post-mortems when performance degrades.
The models that stay useful are the ones operated as products, not delivered as artefacts.
Toolkit
Framework, platform and vendor selection is driven by the shape of the work and the team who'll own it, not by whichever ecosystem is loudest this year. The list below is representative, not prescriptive.
- Modelling
- Python · PyTorch · TensorFlow · XGBoost · scikit-learn
- Data
- Snowflake · Databricks · dbt · Feast
- MLOps
- MLflow · SageMaker · Vertex AI · Weights & Biases
- Serving
- Kubernetes · Ray · Triton · vLLM
- Monitoring
- Evidently · WhyLabs · Datadog
Selected work
What clients ask us first
01How do you frame a modelling engagement?
From the decision workflow first, who acts, when, with what tolerance. The model follows. Great models in the wrong workflow waste quarters.
02How complex should the model be?
As simple as beats the baselines and meets the workflow's needs. Complex only when evidence demands it. Simpler models are cheaper to deploy, faster to retrain and easier to explain.
03How do you handle drift?
Monitoring that catches data and prediction drift, ground-truth capture where possible, retraining on a real cadence, champion/challenger deployments. Drift is a signal, not a failure.
04How do you handle explainability and governance?
Model cards, feature-importance analysis, bias assessment where relevant, and audit trails on decisions. Aligned to sector-specific frameworks where they apply.
Let's discuss your predictive-analytics programme.
Share a brief overview of the decisions you want to move, we'll arrange an introductory conversation with the relevant practice leadership.