A model in a notebook changes nothing.
Predictive models, recommendation engines and NLP built on your real data, from customer segmentation to demand forecasting. Deployed with monitoring, retraining pipelines and explainability from day one.
Azure
Where this sits in the three stagesBuilt inside Stage 2, Build; scoped in Assess.
Accuracy in a notebook is not the same as value in production. We build the models that make it there.
Demand, revenue and risk forecasts with confidence intervals leadership can plan against.
Engines that lift conversion and retention, tuned to the metrics that matter to the business.
Classification, extraction and search over unstructured text, grounded in your own corpus, with audit trails.
Catch outliers and threats early, balancing recall against friction.
InsuranceProducing the weekly risk report
Screening inbound deals
Runs on
Commercial real estatePricing a property for a client
Experian location dataRuns onWe train and serve on the lakehouse, cloud and models your risk and security teams already approve.
DatabricksEvery model ships with the monitoring and retraining that keeps it useful, and the explainability that lets the business trust what it recommends.
Drift is watched in production, so performance does not erode after launch with nothing watching for it.
Retraining pipelines, not one-offs, so models stay accurate as the data moves.
Decisions you can defend. Predictions the business cannot explain go unused.
Predictive models, recommendation engines and NLP built on your real data, from customer segmentation to demand forecasting, deployed with monitoring, retraining pipelines and explainability from day one. Four kinds of work: predictive and forecasting models, recommendation and personalization, NLP and document intelligence, and anomaly and fraud detection.
Yes. We build demand, revenue and risk forecasts with confidence intervals leadership can plan against. Accuracy in a notebook is not the same as value in production, so we build the models that make it there, with the monitoring and retraining that keeps them useful.
Drift is watched in production, so performance does not erode after launch with nothing watching for it, and retraining pipelines, not one-offs, keep models accurate as the data moves. A model in a notebook changes nothing; every model ships with the monitoring and retraining that keeps it useful.
Yes. Every model ships with the explainability that lets the business trust what it recommends: decisions you can defend. Predictions the business cannot explain go unused.
We build on Claude and Databricks. We train and serve on the lakehouse, cloud and models your risk and security teams already approve. Analytics and ML work is built in Stage 2, Build, and scoped in Stage 1, Assess.
For The Hartford we put claims triage models in production with a full audit trail, saving $6M+. The system runs on Databricks on AWS with Tableau and sits at Level 3 (L3).
