Enterprise RAG
RAG for enterprise documents and business data
ChatWithDB gives teams a single workspace for retrieval over documents, spreadsheet exports, and database-backed records. Ask questions in plain English, then verify each answer against cited source context before you reuse it.
Built for private knowledge that lives across formats
Enterprise RAG is most valuable when the answer is split across different file types, teams, and systems. ChatWithDB is designed for that mix: it can search the source material, pull back supporting context, and help users verify the result instead of trusting a generic summary.
Policy and SOP libraries
Ask questions across internal policies, playbooks, and operating manuals without hunting through folders or PDF viewers.
Contracts and account docs
Find clauses, obligations, renewal dates, and account-specific details with answers grounded in the source documents.
Business data plus documents
Combine spreadsheet exports, customer records, and supporting documents when the answer lives across multiple systems.
How enterprise RAG works in ChatWithDB
Upload or connect the documents and business files you want to search.
Ask a natural-language question about the topic, metric, or record you need.
Review the cited answer and follow the source context back to the original file or row.
Best-fit enterprise RAG use cases
- Search across policy libraries, SOPs, onboarding guides, and team playbooks.
- Ask questions over contracts, proposals, and account documents while keeping source evidence visible.
- Combine document search with CSV and Excel exports when the answer depends on both narrative and structured data.
- Reduce manual lookup work without losing the ability to audit the underlying source material.
Enterprise RAG FAQ
What does RAG mean for enterprise documents?
RAG means retrieval augmented generation: the system searches your uploaded or connected content first, then uses that evidence to answer the question with citations.
Is ChatWithDB only for PDFs?
No. ChatWithDB works across PDFs, CSVs, Excel files, Word documents, plain text, and database-backed records, which makes it better suited for mixed business knowledge.
When should I use this instead of natural language SQL?
Use this page when the answer depends on document context as well as structured data. Use natural language SQL when the question is primarily about database-backed rows and metrics.
Can I verify the answers?
Yes. ChatWithDB is built around cited answers so teams can inspect the original source context before reusing the result in a report or workflow.
Put your enterprise knowledge to work
Start with the demo, then use ChatWithDB when you want a single workspace for documents, spreadsheets, and database-backed answers with citations attached.
Explore the demo