Enterprise RAG
AI that answers from your documents, with sources.
We build retrieval-augmented generation systems that search your policies, contracts, manuals, tickets and ERP data, answer in plain language and show exactly where each answer came from. Deployed on your infrastructure when it has to be.
What we build
Common enterprise RAG systems.
Policy and SOP assistant
Staff ask questions; answers cite the clause and version.
Contract and legal search
Find obligations, renewal dates and risky clauses across thousands of agreements.
Customer-support knowledge
Agents and bots answer from current manuals and past tickets.
Audit and compliance evidence
Locate evidence for a control across documents and systems.
Finance and ERP questions
Plain-language questions over ledgers and reports, with links to the records.
Multilingual retrieval
Questions in Hindi and other Indian languages over English documents.
How we make it reliable
- Document parsing that keeps tables, headings and page numbers
- Permission-aware search: users only see what they are allowed to
- Citations on every answer, and refusal when sources are missing
- An evaluation set agreed with your team before launch
- Monitoring and re-testing when models or documents change
Deployment options
- Your cloud account in an Indian region
- On-premise servers for regulated data
- Routed through MNB Intelligence for logging and model choice
- Connectors for SharePoint, Google Drive, email, Tally, SAP and Zoho
See it on your documents.
Share a sample set. We will show a working prototype and the evaluation results before you commit.
Questions
Frequently asked.
What is RAG?
Retrieval-augmented generation: before answering, the system searches your documents and data, then the language model answers using only what it found, with references back to the source.
Why not just fine-tune?
Fine-tuning changes how a model writes and reasons; it is a poor way to store facts that change. RAG keeps answers current because it reads the latest documents every time. Many systems use both.
How do you stop wrong answers?
By testing retrieval and answers against an evaluation set your team agrees, by requiring citations, by refusing to answer when sources are missing, and by monitoring answers in production.
Where can the data stay?
On your cloud in an Indian region or on your servers. The search index, documents and logs stay with you.