A knowledge graph RAG (KG-RAG) is a retrieval system built around structured relationships — not a generic chat bot. For these packages, I build a custom graph over the materials that matter for your work: regulations, standards, and guidance applicable to your product, plus your team’s actual design and quality artifacts as they exist today. That combined context is what I use to draft procedures and templates.

The graph is iterative. As objective evidence evolves — user needs, design inputs and outputs, test results, review records — it goes into the graph too. That can update the SOPs, especially how each client’s stage-gate process maps to engineering design reviews. Domain experience keeps the output useful: the goal is not to generate text, but to align your team’s natural work with required, acceptable objective evidence that belongs in a Design History File. AI speeds search and drafting; judgment about what counts as evidence stays with people who know the product.

These fixed-price kits are productized ways to start that work. Most of my engagements remain Time & Materials; the kits are a clear entry point when you want a defined deliverable and a demonstration of how engineering documentation and augmented intelligence fit together.