What We Do

A structured assessment of your data landscape, infrastructure, and team capabilities. We identify where AI/ML can deliver value and build a clear roadmap for getting there. No commitment required beyond the conversation.

• Structured review of your current data sources, pipelines, and infrastructure
• Assessment of team capabilities and tooling gaps
• Identification of high-value ML opportunities ranked by feasibility and ROI
• Technology recommendations tailored to your stack and scale
• A written roadmap with clear next steps and time estimates

End-to-end machine learning engineering — from problem framing and data pipelines through model development, evaluation, and production deployment on AWS or your preferred cloud. NLP, forecasting, classification, anomaly detection, and more.

• Problem framing and success metric definition
• Data pipeline design and ETL engineering
• Feature engineering and model development (scikit-learn, PyTorch, Hugging Face)
• Model evaluation, validation, and bias assessment
• Production deployment on AWS SageMaker, Lambda, or containerized infrastructure
• MLOps setup: monitoring, drift detection, retraining triggers
• Full documentation and knowledge transfer

When your ML work needs a user interface, dashboard, or API to be useful to the humans who depend on it. Built with the same engineering rigor as the models themselves.

• React / Next.js dashboards and data visualization
• REST or GraphQL API design and implementation
• Internal tools for ops, analyst, or engineering teams
• Client-facing applications with authentication and role management
• Integration with existing systems (CRMs, data warehouses, third-party APIs)
• Deployment to Vercel, AWS Amplify, or your preferred hosting