b10116006-art/mes-rag-assistant

The project is in its absolute infancy (2 days old, 0 stars, 0 forks), positioning it as a personal experiment or a very early-stage prototype. While the semiconductor manufacturing niche is highly specialized and generally ignored by frontier labs (OpenAI/Anthropic), the 'defensibility' here is non-existent from a software perspective. In the industrial AI space, the moat is never the RAG logic itself, but the proprietary data connectors, MES integration hooks, and the domain-specific ontology used to structure engineering knowledge. This project aims at a valuable vertical but currently lacks the 'data gravity' or integration surface to compete with established industrial software providers like Applied Materials, ASML, or specialized MES vendors who are likely building similar internal capabilities. Platform risk is low because hyperscalers (AWS/Azure) provide the bricks, but not the specific 'fab-aware' logic. The main threat is displacement by generic but highly-customizable RAG platforms or internal corporate tools developed by the chip manufacturers themselves.