XcellAI| AI Vector Database

It gives models fast access to the most relevant items by meaning — the retrieval step behind RAG chatbots, semantic search, recommendations and similarity checks — without your team building and operating the index.

Yes. The database stores vectors, so anything an embedding model can encode works: text, images, audio or mixed-media embeddings. Each vector is stored with its metadata and a link to the source file.

Yes. Professional runs as a three-node high-availability cluster; Enterprise adds sharding, DiskANN or quantisation for very large collections, a DR copy in a partner DC in India* and custom SLAs, sized after a review.

Typically milliseconds for top-k queries on a well-sized HNSW index*, but latency depends on collection size, dimensions, filters, recall target and hardware. We measure p95 latency and recall on your own test questions before go-live rather than quote a number in advance.

Yes. The engines we run have integrations for common RAG frameworks such as LangChain and LlamaIndex*, and work with models served on our serverless or dedicated inference endpoints, or with hosted model APIs.

Yes — it is the knowledge layer of a RAG chatbot. If you want the chatbot built too, our AI Apps Builder connects to it with no code.

Private network endpoints, TLS in transit, encryption at rest, role-based access with API keys, per-tenant namespaces, document-level access labels and audit logs, monitored by our 24×7 SOC. Data stays in India.

Approximate nearest-neighbour indexes: HNSW graphs for most workloads, IVF with optional product quantisation for compressed large collections, and DiskANN-style SSD indexes for very large ones, plus BM25 or sparse vectors for hybrid search. Similarity is cosine, dot product or Euclidean distance.

If your data already lives in PostgreSQL and the collection is in the low millions, pgvector keeps things simple with SQL joins. For larger collections, heavy filtering, multi-tenancy or hybrid search at scale, a dedicated engine usually fits better. The engine picker above gives a first answer.

GPU servers for embedding and re-ranking — NVIDIA L4, RTX PRO 6000 or H200 — are priced on quote because the price depends on GPU class, count, term and how much is managed. See AI Factory and Dedicated AI Servers.

Book a sizing review. We agree data sources and test questions, recommend an engine and plan, and send an INR quotation. A typical Professional deployment is live in about three weeks*; setup is ₹24,999* one-time, excl. GST.