In Progress

The Physics of Legacy RAG

Why plugging an LLM directly into a 20-year-old ECM API is a disaster, and how to safely vectorize enterprise dark data.

ArchitectureData StrategyLegacy Systems

✍️ Active Architecture Draft: Currently benchmarking latency limits on legacy SOAP APIs. Full teardown coming soon.

The Problem

The most common mistake in Enterprise AI today is the "naive RAG" implementation.

Engineering teams point modern AI frameworks directly at the APIs of 20-year-old legacy systems (like IBM FileNet). Within an hour:

  • The legacy database CPU spikes to 100%.
  • Production banking workflows halt.
  • DBAs frantically kill the AI's connection.

Legacy systems were built for targeted human retrieval, not parallel machine polling.

What This Paper Will Cover

To safely unlock enterprise "dark data," we must respect the physical constraints of legacy hardware. This upcoming research outlines:

  • The Anti-Corruption Layer: Using Change Data Capture (Debezium) and Kafka to decouple the AI from the core system.
  • The ACL Nightmare: Why flattening legacy security permissions into vector databases creates massive compliance breaches.
  • Late-Binding Auth: A 5-step flow to guarantee pgvector searches respect real-time legacy permissions.