Points clés
- Corporate RAG = internal knowledge base. Upload all corporate documents, let employees ask questions.
- Use cases: Policy lookup, contract Q&A, research discovery, onboarding, compliance training.
- Scale: 10k–100k documents, 100–500 concurrent users, <2 sec latency.
- Local advantage: Proprietary documents never leave your network. Full audit trail of who accessed what.
- As of April 2026, corporate RAG saves companies $500k–5M annually in employee productivity.
What Documents Can Corporate RAG Handle?
| Document Type | RAG Use | Typical Users |
|---|---|---|
| Employee handbook | — | — |
| Contracts | — | — |
| Technical docs | — | — |
| Research papers | — | — |
| Compliance docs | — | — |
| Customer docs | — | — |
How Do You Ingest Documents at Scale?
Ingestion pipeline converts documents to embeddings and stores in vector DB.
- 1Extract documents: From file servers, SharePoint, Jira, Confluence, etc.
- 2Parse: Convert PDFs, Word docs, HTML to text. Handle tables, images.
- 3Chunk: Split into 500–1000 token chunks with 20% overlap.
- 4Embed: Convert chunks to vectors using local embedding model (nomic-embed-text).
- 5Index: Store vectors in Qdrant, Milvus, or Weaviate with metadata (source, date, author).
- 6Refresh: Weekly or monthly re-ingest to capture updates.
How Do You Design Multi-User Corporate RAG?
Typical stack:
- Frontend: Web interface or Slack bot.
- API: REST endpoint for RAG queries.
- LLM: Local Llama 13B (quality) or 7B (speed).
- Embeddings: Local nomic-embed-text (or cloud for speed).
- Vector DB: Qdrant (distributed) for 10k+ documents.
- Document storage: Encrypted file server for PDFs and sources.
- Access control: LDAP/AD integration for user permissions.
How Do You Ensure Retrieval Quality?
Poor retrieval = poor answers. Quality depends on:
- Chunking strategy: Semantic chunks (by topic) outperform fixed-size chunks.
- Embedding model: Use domain-specific embeddings if available. Generic embeddings may miss domain terminology.
- Retrieval parameters: k=5–10 (how many chunks to retrieve). Too low = missing context. Too high = noise.
- Reranking: Use cross-encoder to re-rank chunks by relevance (small quality boost).
- User feedback: "Feedback" button on answers. Use to tune retrieval parameters.
How Do You Implement Governance and Access Control?
Corporate RAG must track access for compliance:
- Access logs: Who queried what documents, when, from where.
- Retention: Keep logs for 3–7 years (regulatory requirement).
- Access control: Restrict documents by role (e.g., only legal sees contracts).
- Audit: Quarterly review of access logs for unusual activity.
- Data classification: Mark documents as public, internal, confidential, restricted.
Common Corporate RAG Mistakes
- Ingesting without cleaning. Old documents, duplicates, test files = retrieval noise. Clean before ingesting.
- Not chunking intelligently. Fixed-size chunks split topics mid-sentence. Use semantic chunking.
- No access control. If all documents are visible to all employees, confidential info leaks.
- Ignoring retrieval quality. Test with real employees before wide rollout. 50% of issues are retrieval, not generation.
- Not re-ingesting updates. Document database becomes stale. Schedule weekly/monthly re-ingest.
What Are Common Questions About Corporate RAG?
How many documents can corporate RAG handle?
Depends on average document size and latency. Typical range: 10k–100k documents. Retrieval latency should be <1 second. If slower, optimize chunking or embeddings. Test with your actual document set.
Which embedding model should we use?
Open-source options: all-MiniLM-L6-v2 (fast, good), BAAI/bge-base-en-v1.5 (better quality). Proprietary: OpenAI text-embedding-3-small. For local deployment, use open-source. Quality difference matters: better embeddings = better retrieval.
How do we update documents without losing chat history?
Store chat history separately from document embeddings. Update embeddings on a schedule (weekly/monthly). Old chats still reference old document versions, which is fine—just document the version date.
Can we use RAG for confidential documents?
Yes—local RAG is ideal. Documents stay on-premises, queries are not logged externally, and you control access via role-based permissions. This satisfies HIPAA and GDPR.
What is semantic vs fixed-size chunking?
Fixed-size (e.g., 512 tokens) is simpler but splits topics mid-sentence. Semantic chunking uses sentence/paragraph boundaries, preserving meaning. Semantic is better for RAG quality but slower to set up.
How do we measure RAG quality?
Metrics: retrieval@k (right document in top k results), latency (should be <1 sec), user satisfaction (survey employees). Test with domain experts—they know what "correct" answers look like.
Sources
- LlamaIndex Documentation — docs.llamaindex.ai
- Qdrant Vector Database — qdrant.tech
- Retrieval Evaluation — arxiv.org (search "RAG evaluation metrics")