Augmented RAG for R&D

Context and objectives

Build a production-grade generative AI tool for competitive intelligence on aircraft engines, using public flight-incident data (BEA, NTSB, FAA, EASA, etc.), beyond the reach of classical RAG.

The challenge: a vast, fragmented body of public data.

• Eight types of documents: incident reports, patent files, competition reports, press articles, safety directives, academic research, type certificate data sheets and general search.
• Heterogeneous, noisy data: free text, multilingual, large volumes. Classical RAG lacks precision and exhaustiveness.
• A high-stakes need: R&D, audit and flight safety teams need answers they can trust and audit.

Outcome

Live in the client's sovereign cloud: engineers run exhaustive, auditable queries with citations, and the ingestion pipeline scales the knowledge base autonomously.

Our approach

An advanced RAG architecture combining a knowledge graph and agentic retrieval:

• A knowledge graph aligned with the engineering taxonomy: 88 structured tags and more than 80 fields per document.
• A hybrid ingestion pipeline: LLMs and classical NLP populate the graph automatically and at scale.
• Agentic retrieval: SQL-grade tag queries on top of the graph, producing exhaustive listings with citations.