Neural 360 ingests sales, social, reviews, feedback, tickets, complaints and docs into one MongoDB Atlas store — then answers plain-English questions about any customer with a fused behavioral dashboard.
What a customer buys, says, rates, complains about and struggles to understand resolves into a single explainable score — and a clear picture of what they'll do next.
Structured, keyword and vector search run in parallel and fuse with RRF, so no single method's blind spot survives.
INT8-quantized embedding and retrieval keep ingestion and search fast on commodity hardware.
The model reads the live schema, classifies intent, and writes its own MongoDB query. You never hand-write one.
Answers render as a table, card, ranked list or summary — whichever fits the data — never raw JSON.
A PDF page, a CSV row and a 384-d embedding live as siblings in a single MongoDB Atlas collection.
Every document carries a customer scope; the filter is injected at query time and never trusted to the model.
Any format — CSV to PDF to embeddings — normalized into one Atlas schema, deduped by content hash.
Three retrievers fire in parallel, fuse with RRF, then re-rank for relevance.
Behavioral signals roll up into health, risk, growth and adoption scores.
The answer is formatted for a human and traced back to its source domains.
Illustrative targets from the pilot — replace with your measured values.