Enterprise RAG and Model Gateway Development

The Challenge

A leading retail enterprise required a centralized platform to streamline its rapidly expanding machine-learning initiatives. Multiple business units were independently developing predictive models, causing redundant workloads, inconsistent governance, and fragmented deployment pipelines. This created operational inefficiencies and made compliance with corporate AI-governance standards difficult.

Our Solution

Our team architected a Databricks-powered Enterprise RAG solution – a unified environment integrating data preparation, model training, evaluation, and deployment under one scalable framework with AI Guardrails. The planning stage emphasized modular MLOps design with strong governance controls using Unity Catalog for access management, lineage tracking, and versioned datasets. We planned a custom AI Gateway that orchestrated model serving through a consistent API layer, enabling teams to reuse pre-validated models and deploy new ones through automated CI/CD pipelines. The workflow leveraged MLflow Evaluate for model performance monitoring and Delta Live Tables for automated data refresh, ensuring each production model remained synchronized with the latest data streams. The resulting architecture transformed the enterprise's AI operations into a continuous innovation pipeline with auditability, scalability, and governance at its core.

Results & Outcomes

Technologies Used