Services

Problems Solved

• Legacy warehouse and pipeline patterns no longer meet current analytics needs
• Source systems are fragmented across operational platforms and teams
• Data platform standards are inconsistent across environments

Typical Engagement

Current-state review, target architecture design, modeling approach, ingestion and transformation patterns, and implementation guidance for Azure or Snowflake-based platforms.

Who It's For

Architects, data engineering leads, platform teams, and technology decision-makers.

Deliverables

• Reference architecture and integration pattern recommendations
• Modeling and layer design guidance
• Migration and implementation considerations
• Architecture risks, decisions, and next steps

Problems Solved

• AI proof-of-concepts are disconnected from enterprise data platforms
• Teams need a realistic path from experimentation to production
• Solution design must account for integration with existing Azure assets

Typical Engagement

Solution architecture for Azure AI and Foundry-based workloads, integration with data services such as Databricks, and implementation guidance for resilient enterprise deployment.

Who It's For

Architecture teams, innovation groups, and delivery teams building enterprise AI capabilities.

Deliverables

• AI solution architecture and deployment design
• Integration blueprint for Azure services and data sources
• Production-readiness considerations and risks
• Implementation recommendations for delivery teams

Problems Solved

• Models are difficult to operationalize beyond notebooks and experiments
• Security, access control, and deployment concerns slow down ML adoption
• Teams need a practical monitoring and retraining pattern

Typical Engagement

Architecture and implementation guidance across data preparation, model training, containerization, Azure Machine Learning, AKS deployment, monitoring, and operational controls.

Who It's For

Data scientists, ML engineers, platform teams, and technical leaders.

Deliverables

• MLOps architecture and deployment recommendations
• Environment, security, and monitoring considerations
• Model lifecycle workflow design
• Implementation roadmap for production ML