Document ingestion
PDFs, docs, Notion, Drive, Confluence, websites and databases.
Answers from your knowledge, with sources.
We connect language models to your own knowledge so answers are accurate, current and cited. Your team and customers get answers without digging through folders.
Planning
Goal
Output
Working…
- Every answer cited
- Follows your permissions
- Says so when it doesn't know
How RAG works
Your documents in. Cited answers out.
- 01
Connect
We link your sources: files, wikis, help center and databases.
- 02
Index
Content is split into passages and indexed for meaning, not just keywords.
- 03
Retrieve
Each question pulls the most relevant passages from your data.
- 04
Ground
The model writes its answer using only those passages.
- 05
Cite
Every answer links back to the paragraph it came from.
What we build
A knowledge layer your team can trust
Semantic search
Vector and hybrid search tuned to how your team actually asks questions.
Cited answers
Every answer links back to the source paragraph.
Access control
Users only see answers from documents they're allowed to open.
Always in sync
Scheduled re-indexing keeps knowledge current.
Quality evaluation
Test sets that measure retrieval accuracy and answer quality.
Use cases
Where teams use it first
InternalCompany wiki assistant
Staff ask questions in Slack or Teams and get cited answers from policies and SOPs.
Slack→Confluence→Drive
SupportHelp-centre answers
Customers get direct answers from manuals and FAQs instead of a list of links.
Help center→Manuals→FAQs
Legal & financeContract Q&A
Search across contracts and reports and pull out clauses, dates and figures.
Contracts→Reports→SpreadsheetsRAG vs. the alternatives
Why not just search, or just a chatbot?
| Capability | Keyword search | Chatbot without your data | RAG assistant |
|---|---|---|---|
| Answers in plain language | No | Yes | Yes |
| Uses your private documents | Yes | No | Yes |
| Shows where answers come from | Links only | No | Yes, inline citations |
| Stays up to date | Yes | No | Yes, re-indexed on a schedule |
| Respects document permissions | Yes | No | Yes |
Knowledge products we've shipped
Running in production today
Semantic searchIntelliVerse X Chat
A product-recommendation chatbot that uses NLP and FAISS semantic search to find and rank products from a live catalogue.
RAG search
Fashion AI E-Commerce
A multi-brand fashion platform with a shopping assistant and semantic, RAG-powered product search.
Document AISnapMedia
Extracts data from structured PDFs with OCR and turns it into editable insights and video summaries.
Process
From first call to production
01
Scope
A short workshop to pick the use case, define what success looks like and check your data.
02
Prototype
A working demo on your real data within weeks, so you can see it before you commit.
03
Build & integrate
A production build connected to your tools, with guardrails, tests and monitoring.
04
Launch & improve
We go live, track quality and cost, and keep tuning as usage grows.
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Questions