I came up with Vrin after seeing the same failure pattern in three very different environments: big tech, enterprise retail, and healthcare. At Microsoft, I built internal AI assistants for engineers and watched vanilla RAG collapse under real workloads—bloated contexts, missing cross-document connections, and no provenance. With a global retail brand, I saw how hard it was to answer what should have been simple, high-stakes questions about orders and inventory because the facts lived in different systems and no tool could reliably “stitch the story” together. In my health LLM work, I had to design systems that pulled the right clinical signals, chained them correctly, and justified every step, which made it obvious that the real bottleneck wasn’t just the model—it was how knowledge was stored and structured. Vrin is essentially the system I kept wishing existed in all of those roles: a deep-search and action engine that fixes the data layer first so reasoning, evidence, and governance actually work.
