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Connect a domain and Neural 360 parses, normalizes and deduplicates it into a single unified collection.
Drop a file in any supported format. Neural 360 parses, normalizes, embeds and lands it in the unified collection — indexes are created automatically and the live pipeline timeline below shows you every step.
Every record — a sales row, a support conversation, a chunk of a PDF — is normalized into the same document schema. That's what lets a single query span all of them.
Transactional rows keep their typed fields and become filterable, aggregatable documents — no star schema, no warehouse load.
// sales transaction { "type": "sales_order", "customer_id": "cus_8842", "amount": 14250, "sku": "PREMIUM-AN", "ts": "2026-05-21" }
Conversations, tickets and reviews land as free text with an embedding alongside, so they're searchable by keyword and meaning.
// support conversation { "type": "support_ticket", "customer_id": "cus_8842", "content": { "text": "billing sync failed after May update…" }, "embedding": [0.021, -0.44, …] }
Long documents are split into passages before embedding, so retrieval points to the exact clause — not a whole 40-page contract.
// one chunk of a contract { "type": "doc_chunk", "parent_id": "doc_renewal_22", "chunk": 7, "of": 31, "content": { "text": "auto-renews for 12 months unless…" } }