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Chat With Your Data
Ask questions in plain language. Get answers from the right document, with a citation, not a list of search results to dig through.
We build custom retrieval-augmented generation (RAG) systems on top of your existing data sources: SharePoint, Confluence, Google Drive, spreadsheets, and internal wikis. Each system is permission-aware, cited, and tuned for the specific structure of your data.
Generic chatbots don't know your document structure, your naming conventions, or your permission model. A custom build does.
Tell us what data source you want to search.
Each page below covers one integration in detail, what the system does, how it handles permissions, and what we build.
RAG on Microsoft 365
Natural language search across SharePoint documents, libraries, and pages, with permission-aware retrieval so employees only find what they have access to.
RAG on Atlassian
Ask questions of your Confluence wiki in plain language. Engineers find architecture decisions; support teams find troubleshooting steps, with direct page citations.
Text-to-analysis on spreadsheets
Ask "What was our Q3 gross margin by product line?" and get the answer, without pivot tables or SQL. Works on Excel, Google Sheets, and CSV data.
RAG on Drive / Box / Dropbox
AI search across Google Drive, Box, or Dropbox. Ask questions across PDFs, Docs, and presentations. Get answers with a link to the exact clause or paragraph.
RAG on Notion, Guru, Slab
Custom AI search over Notion, Guru, Tettra, Slab, or any internal knowledge base. Employees get instant answers from documented procedures, without pinging colleagues.
Generic AI search products connect to your data source and index everything together. That works for small, flat knowledge bases. It breaks down when your data has complex permissions, inconsistent naming, multiple content types, or domain-specific terminology your employees use in questions.
A custom RAG system is tuned to your data shape. The chunking strategy matches your document structure. The metadata filters match your folder hierarchy. The retrieval pipeline is tuned against questions your team actually asks, not generic benchmarks.
Most importantly, a custom build respects your permission model. If a document is restricted to a specific team in SharePoint or Confluence, a custom system enforces that at search time. A generic tool may not.
Users only retrieve documents they have access to. The AI enforces your existing ACLs at query time, not just at index time.
Every answer includes a reference to the source document and section. Employees can verify the answer and follow the link directly.
Internal acronyms, product names, team names, a custom system is configured with your terminology so queries using internal language resolve correctly.
Slack bot, Teams bot, embedded web widget, or standalone app. Built for the interface your team already uses, not a new tool they have to remember to open.
Describe what data your team wants to search and how they want to ask questions. We'll tell you which integration makes sense, or whether a multi-source build is the right approach.