Data, Memory & Knowledge. RAG

Enterprise RAG That Actually Works at Scale

Retrieval-augmented generation tuned for enterprise accuracy, 95% answer precision, sub-2-second responses, 100+ data source connectors, and every answer cited.

RAG Pipeline
User Question
Natural language
Retriever
Vector search
Context
Top-k chunks
LLM
Generate answer
Response
Cited answer
Answer: "The refund policy allows returns within 30 days..." [Source: policy-v3.pdf p.4]
95%
Answer accuracy
< 2s
Response time
100+
Data sources
Always
Cited

Overview

What is enterprise RAG?

Basic RAG gives LLMs access to documents. Enterprise RAG makes those answers accurate, fast, and trustworthy at scale. It combines advanced retrieval, hybrid search, re-ranking, query expansion, with citation enforcement, source access controls, and continuous freshness so employees and customers always get correct answers grounded in your latest content, not hallucinations.

What's included

Hybrid retrieval

Combine dense vector search with BM25 keyword matching and re-ranking to surface the most relevant chunks for every query.

Citation enforcement

Every answer includes source references with document name, page, and paragraph. Uncited hallucinations are detected and suppressed.

Access-controlled retrieval

Retrieval is user-aware. Documents the querying user doesn't have permission to see are never surfaced in their answers.

100+ connectors

Ingest from Confluence, Notion, Google Drive, SharePoint, Salesforce, Zendesk, GitHub, and 100+ other sources with pre-built connectors.

Query expansion

AI automatically rephrases and expands queries to retrieve relevant content even when the user's phrasing doesn't match the source vocabulary.

Real-time freshness

Ingestion pipelines keep the knowledge base current within minutes of source documents being created or updated.

How it works

From setup to production

01

Connect

Connect your data sources using pre-built connectors. Documents are ingested, chunked, and embedded automatically.

02

Index

Chunks are indexed in the vector store with metadata for access control, source tracking, and freshness management.

03

Retrieve

On each query, hybrid retrieval fetches the most relevant chunks, re-ranks them, and packages them as context for the LLM.

04

Answer

The LLM generates a cited answer from the retrieved context. Accuracy is continuously monitored against a golden test set.

01

Connect

Connect your data sources using pre-built connectors. Documents are ingested, chunked, and embedded automatically.

02

Index

Chunks are indexed in the vector store with metadata for access control, source tracking, and freshness management.

03

Retrieve

On each query, hybrid retrieval fetches the most relevant chunks, re-ranks them, and packages them as context for the LLM.

04

Answer

The LLM generates a cited answer from the retrieved context. Accuracy is continuously monitored against a golden test set.

FAQ

Common questions

Related

More from this service

Get started

Deploy enterprise RAG with 95% answer accuracy

Talk to an expert and get a tailored implementation plan within 48 hours.

Talk to usRequest a demo