Case Study

How a leading U.S. health insurer scaled enterprise data onboarding with Agentic AI platform

OUR CLIENT

Our client is one of the largest health insurance providers in the United States, serving millions of members through a broad portfolio of healthcare plans and managed-care services. Operating in a highly regulated environment, the organization depends on accurate, scalable data integration to support business operations, customer services, and digital transformation initiatives.

THE CHALLENGE

Slow, manual data onboarding was limiting scalability and analyst productivity

The client's source-to-target mapping process relied heavily on experienced business system analysts (BSAs) to manually profile incoming data, discover relationships, define transformation rules, and document mappings for downstream implementation.

• Manual source analysis: Business system analysts (BSAs) manually profiled each new incoming dataset.
• Time-consuming mapping: Source-to-target mapping, transformation rules, and documentation were created manually.
• Data complexity: Multiple data formats, such as CSV, XML, JSON, Excel, fixed-width files, and compressed archives, made integration and standardization difficult.
• Inconsistent outcomes: Manual processes increased the risk of mapping errors and inconsistencies.
• Reduced analyst productivity: Analysts spent valuable time on repetitive tasks instead of high-value analysis.
• Slower data onboarding: Manual effort delayed data integration and limited scalability.

THE TRANSFORMATION

From analyst-intensive data mapping to AI-assisted, governed enterprise onboarding

Rather than incrementally improving manual processes, UST reimagined enterprise data onboarding around a multi-agent AI model. The result was a reusable platform that automated source profiling, metadata generation, transformation logic, and source-to-target mapping while preserving governance, explainability, and human oversight.

• Multi-agent platform: Five specialized AI agents orchestrated through Google Agent Development Kit (ADK) and Vertex AI Gemini for enterprise.
• Automated data discovery: Automated data profiling, business key discovery, and source-to-target mapping.
• AI-assisted mapping: AI-generated metadata and an AI mapping assistant for business system analysts (BSAs).
• Evidence-based recommendations: Retrieval-augmented generation (RAG) using EvidenceHub to deliver evidence-based mapping recommendations.
• Explainable AI: Confidence-scored, explainable recommendations with human-in-the-loop AI workflow.
• Natural language querying: NQL-to-SQL querying capabilities for faster data exploration.
• Enterprise data standardization: Vendor file format standardization for CSV, JSON, XML, EDI, fixed-width files, and other enterprise data formats.
• Production-ready architecture: Enterprise-grade orchestration, governance, privacy controls, and token-efficient AI processing.

The AI-assisted platform empowered analysts to make faster, more informed decisions while maintaining governance and transparency, creating a scalable foundation for future enterprise data onboarding.

THE IMPACT

Accelerating data onboarding while reducing costs, improving consistency, and creating a scalable foundation for enterprise growth

By transforming manual data mapping into an intelligent, scalable capability, the health insurer reduced AI costs, accelerated onboarding, and improved analyst productivity. It established a repeatable foundation for future enterprise data integration.

Explore how UST helps healthcare organizations accelerate enterprise data integration with AI-powered data engineering, intelligent automation, and governed analytics platforms.

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