Data Science Services

We run a short feasibility assessment before any major investment. It checks history length, label availability, data quality, how definitions changed over time and whether features are available at the moment a prediction is needed. We then train a quick baseline model to see whether there is a usable signal. The result is a clear recommendation: proceed, improve data first, or choose a different use case, so you avoid funding a model the data cannot support.

A feasibility assessment usually takes two to four weeks. Developing and validating a first production-candidate model typically takes another one to three months, depending on data complexity and integration needs. Deployment and a controlled pilot follow. We deliver in sprints with weekly written updates and demos, so you see intermediate results such as data findings and baseline accuracy early, and can redirect the work if priorities change.

Cost depends mainly on data preparation effort, the number of models and segments, integration requirements, latency needs (batch versus real time), and the level of monitoring and support required after go-live. Cloud compute for training and inference is usually modest compared with engineering effort, but we estimate it explicitly. We provide a phased proposal so you can fund feasibility first and commit to production only once value is demonstrated.

We check training data for representation gaps, evaluate model performance across relevant segments and avoid sensitive attributes or close proxies where they are not appropriate. We use explainability techniques such as SHAP values to show which factors drive individual predictions and overall behavior. For regulated decisions, such as credit, we document model design, assumptions, limitations and validation results in a format suitable for internal model risk review.

You do. Models, code, feature pipelines and documentation are delivered into your repositories and cloud accounts, and intellectual property for client-specific work transfers to you under the contract. We avoid proprietary black-box components so your team can maintain, retrain or replace models independently. If you prefer, we can continue to operate and improve the models as a managed service with defined service levels.

Yes. We regularly deploy on Databricks, Azure Machine Learning, Amazon SageMaker, Google Vertex AI, Snowflake and Kubernetes-based environments. We prefer to use the platform your team already runs, adding MLOps components such as MLflow for tracking and a registry where they are missing. If no platform exists, we recommend a right-sized option based on your cloud strategy, data volumes and skills, and explain the trade-offs.