Enterprise RAG
Transform your organization's internal knowledge into a secure, queryable intelligence layer. Enterprise RAG enables your teams to ask natural language questions against millions of internal documents — with accurate, cited, auditable responses.
The eLightInfo RAG Architecture
A production-grade RAG system is far more than a vector database and an LLM. Our architecture addresses ingestion, retrieval quality, security isolation, and evaluation at enterprise scale.
Document Ingestion Pipeline
Automated connectors for PDF, Word, Excel, PowerPoint, HTML, SharePoint, Confluence, Salesforce Knowledge, and custom APIs. Intelligent chunking strategies (semantic, recursive, agentic) with metadata preservation for rich filtering during retrieval.
Embedding & Vector Store
High-quality embedding models (text-embedding-3-large, Cohere embed-v3) with optimized chunk sizes per document type. Stored in your preferred vector database: Pinecone, Weaviate, pgvector (PostgreSQL), or Amazon OpenSearch. All data remains in your cloud account.
Hybrid Retrieval Strategy
Combine dense vector search with BM25 keyword search (hybrid retrieval) for better recall. Cross-encoder reranking to ensure the most relevant chunks top the list. Metadata filtering (by date, department, document type, classification level) for precision.
Secure Generation Layer
LLM generates responses grounded in retrieved context with mandatory citation. Hallucination detection via faithfulness scoring. User-level access controls ensure responses only reference documents the querying user is authorized to see.
Evaluation & Continuous Improvement
RAGAS-based automated evaluation: answer faithfulness, context relevancy, and answer relevancy tracked continuously. Human evaluation loop for quality sampling. Retrieval quality dashboards for ongoing tuning.
Supported Document Sources
Document Files
PDF, Word (.docx), Excel (.xlsx), PowerPoint, plain text, Markdown
Microsoft 365
SharePoint Online, Teams channels, OneDrive, OneNote
Atlassian Suite
Confluence spaces and pages, Jira tickets and comments
CRM & Custom APIs
Salesforce Knowledge, ServiceNow, custom REST/GraphQL APIs
Supported Vector Stores
Pinecone
Managed, serverless vector database with sub-millisecond query latency
Weaviate
Open-source with built-in hybrid search and multi-tenant isolation
pgvector (PostgreSQL)
Vectors in your existing Postgres database — no new infrastructure
Amazon OpenSearch
AWS-managed hybrid search with k-NN and BM25 in a single service
Multi-Tenant RAG with Document-Level Access Control
In enterprise environments, different users and teams should only be able to query documents they are authorized to access. Our RAG implementation enforces document-level access controls at query time — not just at ingestion time.
Users authenticated via your IdP (Okta, Azure AD, Cognito) receive a permission context that filters the vector search to their authorized document set before any retrieval occurs. This prevents cross-tenant data leakage even when multiple business units share the same RAG infrastructure.
All data stays in your cloud account
No document content ever leaves your environment.
End-to-end encryption
Encryption at rest and in transit with customer-managed keys.