Enterprise RAG Chatbot
An enterprise RAG chatbot you can actually ship to production.
Grounded answers, permission-aware retrieval, citations on every response. Built for regulated industries, not weekend prototypes.
Most RAG demos look great until they hit real corporate data: stale documents, conflicting policies, document-level permissions, and answers nobody can audit. Uthereal's enterprise RAG platform handles those problems by default, so your team ships a chatbot that legal, security, and end users all trust.
What you get
Permission-aware retrieval
Inherits source ACLs. Users only see what they're authorized to see, never more.
Citations on every answer
Inline source links with confidence scores. Easy to verify, easy to audit.
Hybrid + re-ranked search
Vector + keyword + structured retrieval, then re-ranked for relevance and recency.
How it works
- 1
Ingest your sources
SharePoint, Google Drive, Confluence, Notion, S3, databases, web. Continuous sync.
- 2
Retrieve and rank
Hybrid search, semantic re-ranking, permission filtering, conflict resolution.
- 3
Generate and evaluate
Grounded responses with citations, evaluation harness, and feedback-driven improvement.
Use cases
- Internal knowledge base chatbot for employees
- Customer support assistant with verifiable answers
- Sales enablement assistant grounded in product docs
- Compliance and policy Q&A for regulated teams
- Research assistant for analysts and consultants
- Field service assistant grounded in technical manuals
Built on Swiss-grade trust
Swiss-hosted
Hosted in Switzerland under FADP/nFADP. EU/US regions available.
No training on your data
Embeddings and conversations stay in your dedicated tenant.
Audit-ready
Per-query traces of retrieval, ranking, and generation. Exportable logs.
Frequently asked
What is an enterprise RAG chatbot?
RAG (retrieval augmented generation) chatbots combine an LLM with a search layer over your private data. The model only answers from what's retrieved, with citations back to the source — so answers are grounded, current, and auditable.
How is this different from a basic vector DB + LLM script?
Production RAG fails on permissions, freshness, conflict, and evaluation. Uthereal handles permission-aware retrieval, hybrid search, re-ranking, multi-source orchestration, response evaluation, and human-in-the-loop review out of the box.
Does it respect document-level permissions?
Yes. We inherit ACLs from your source systems (SharePoint, Google Drive, Confluence, Notion, custom DBs) so users only see what they're already allowed to see.
How are answers kept accurate?
Every response includes citations. Confidence thresholds, fallback flows, structured evaluation, and analytics let you measure groundedness and continuously improve retrieval quality.
Where is the data stored?
Default Swiss hosting under FADP/nFADP. EU/US regions available. No training on customer data. Dedicated tenancy on enterprise plans.
Explore more
From RAG demo to production, in weeks.
Book a working demo on your own content. See real retrieval, real citations, real numbers.