Grounded RAG memory

The concept, in plain terms

Out of the box, a language model only knows what it was trained on — a frozen snapshot that doesn’t include your runbooks, your tickets, or last week’s incident. Retrieval-augmented generation (RAG) fixes this by retrieving the most relevant pieces of your knowledge at the moment of the question and handing them to the model as context. The answer is then “grounded” in sources you can see and trust.

NeurosEdge adds durable memory on top: context that carries across sessions, so the platform remembers what matters to your organization instead of starting cold every time.