Embedding Real-Time RAG Pipelines into Legacy Systems

Why RAG on Legacy Systems Is Different from Greenfield RAG

Legacy enterprise systems present unique RAG challenges: data in proprietary formats (SAP IDocs, Oracle schemas, mainframe flat files), access control that does not map cleanly to modern API patterns, data quality issues that would corrupt a vector index, and compliance requirements that prohibit certain data from entering external model APIs. Building RAG on a greenfield cloud-native system and building RAG on a 20-year-old ERP are fundamentally different engineering problems.

WTA's Legacy RAG Architecture

WTA's standard approach uses MCP (Model Context Protocol) adapters to expose legacy system data as standardised endpoints that RAG pipelines can query without replatforming. Azure Data Factory governs the ingestion pipeline — with PII masking, data quality validation, metadata lineage annotation, and format normalisation. Azure AI Search provides the hybrid retrieval layer. For complex, relationship-rich enterprise knowledge, WTA implements GraphRAG — replacing the flat vector index with a semantic knowledge graph (Neo4j or Azure Cosmos DB) that preserves entity relationships and enables explainable, auditable retrieval. See how WTA modernises legacy platforms with AI agents without replatforming.

Frequently Asked Questions

Can WTA integrate RAG pipelines into SAP without replatforming? Yes. WTA uses MCP adapters to expose SAP data as standardised endpoints for RAG pipelines, eliminating the need for replatforming or complex custom ETL. The adapter layer handles authentication, data format normalisation, and access control mapping.

What is the difference between standard RAG and GraphRAG for legacy enterprise data? Standard RAG retrieves document chunks based on vector similarity. GraphRAG retrieves from a semantic knowledge graph that preserves the relationships between entities — critical for legacy enterprise data where relationships between records (customer → order → product → supplier) are as important as the record content itself.

How does WTA handle PII in legacy RAG pipelines? WTA's ingestion pipelines include automated PII detection and masking before data enters the vector index or knowledge graph. Every ingested document is annotated with provenance metadata including source system, ingestion timestamp, and PII masking status — maintaining a complete audit trail for regulatory review.