RAG Chatbot

Retrieval-augmented generation only works if retrieval is reliable. Qefro combines dense vectors with full-text search and workspace filters for tenant-safe results.

Every answer can cite source documents. When retrieval is empty or weak, the assistant is designed to say it does not know instead of guessing.

Add Business Tools when chat must change state — refunds, lookups, tickets — with encrypted secrets and execution logs.

How Qefro delivers RAG Chatbot

Configure knowledge, instructions, and Business Tools once in the Admin Console. Deploy Customer AI on your website widget and WhatsApp, and Employee AI on a branded Internal Portal — with the same retrieval, permissions, and action layer underneath.

Answers are grounded in your documents and crawled pages. Hybrid search combines keyword and vector retrieval, returns source citations, and is designed to decline when nothing relevant exists — so teams can trust support and internal assistants in production.

When chat must change state — order lookups, tickets, CRM updates — connect your APIs via REST/OpenAPI or the Backend SDK. Credentials are encrypted; outbound calls use HTTPS with SSRF protections; execution logs support review and QA.

Why teams choose Qefro for this use case

You should not rebuild RAG infrastructure, hosting, or channel adapters for every project. Qefro gives organizations a multi-tenant AI Workspace Platform: isolated knowledge per workspace, RBAC for owners/admins/members, PII scrubbing on model calls, and a 14-day free trial with full premium access so you can prove value before buying.

Compare plans on the pricing page, review security controls, and read the benchmark methodology for how we evaluate grounding and refusal behavior. Product docs live at docs.qefro.com.

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