Ground your LLMs in real knowledge at billion-document scale.
Retrieval-augmented generation turns a general-purpose LLM into a precise, domain-specific expert by grounding every answer in your own data. CogniCloud's RAG stack combines GPU-accelerated vector indexing, hybrid BM25 + dense search, real-time ingestion, and a low-latency inference endpoint into a single coherent pipeline.
1B+
Vectors per namespace
<1ms
p99 ANN query latency
10ms
End-to-end RAG pipeline TTFT
∞
Real-time upserts, no downtime
The Challenge
Enterprise RAG fails in production because the components don't talk to each other efficiently: embedding pipelines are slow, vector databases can't keep up with real-time document updates, and the end-to-end latency makes the product feel sluggish. You need all three layers — retrieval, context assembly, and generation — optimised as a unit.
How CogniCloud helps
Combine sparse keyword matching with dense semantic similarity in a single query. Reciprocal rank fusion merges results with configurable blend weights — catching both exact matches and semantic paraphrases.
Build a 100M-vector HNSW index in minutes using cuVS (RAPIDS). Nearest-neighbour search saturates GPU SIMD lanes instead of CPU cores — 20× faster than CPU-only implementations.
New and updated documents are embedded, indexed, and queryable within milliseconds of insertion. No batch ingestion jobs, no index rebuild downtime — your knowledge base is always current.
Long RAG context windows (system prompt + retrieved chunks) are cached at the KV layer. Only the user's question needs fresh computation — reducing cost per query by up to 80%.
Namespace-level isolation ensures complete data separation between tenants. A query from tenant A cannot surface documents from tenant B at any layer of the retrieval stack.
Post-retrieval cross-encoder re-ranking and metadata pre-filtering ensure the most relevant chunks make it into context. Relevance scores are surfaced in the response for observability.
How it works
Push documents to the ingestion API. CogniCloud chunks, embeds, and indexes them automatically using your chosen embedding model.
import { CogniCloud } from "@cognicloud/sdk";
const client = new CogniCloud();
await client.vectorStore.upsert({
namespace: "my-knowledge-base",
documents: [
{ id: "doc-1", text: "...", metadata: { source: "wiki" } },
{ id: "doc-2", text: "...", metadata: { source: "docs" } },
],
model: "text-embedding-3-large",
});
// Queryable in < 10msRun a hybrid search query. CogniCloud returns the top-k most relevant chunks with metadata and relevance scores.
const results = await client.vectorStore.query({
namespace: "my-knowledge-base",
query: userMessage,
topK: 8,
hybrid: { alpha: 0.7 }, // 70% dense, 30% BM25
filter: { source: "docs" },
rerank: true,
});
// p99 latency: 0.8msPass the retrieved chunks as context to the Inference Gateway. The KV-prefix cache makes repeated context nearly free.
const answer = await client.chat({
model: "meta-llama/Llama-3-70B-Instruct",
messages: [
{ role: "system", content: SYSTEM_PROMPT },
{ role: "user", content: buildContext(results, userMessage) },
],
stream: true,
});
// TTFT: 7.2ms (94% cache hit on context)Built on
Billion-scale GPU-accelerated hybrid search and real-time ingestion
Generates grounded answers from retrieved context
Caches repeated RAG context prefixes, slashing cost per query
Serves retrieval and generation from the nearest PoP worldwide
LLM Fine-Tuning
Adapt foundation models to your domain — faster and cheaper.
Production Inference
Serve any LLM to millions of users at sub-10 ms TTFT.
AI for Startups
Move fast, iterate daily — without a dedicated MLOps team.
Enterprise AI
Secure, compliant, and governed AI infrastructure at any scale.
Batch & Offline AI
Process millions of records overnight — at the lowest cost per token.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
No spam. No pricing pitches. We reach out personally to discuss your use case.