Topic
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.
Connect systems or install apps once in the Admin Console, then deploy across Website, WhatsApp, Internal Portal, and API channels — with the same retrieval, permissions, and tool layer underneath. Customer support chat is one application on that platform — not the whole product.
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 an External SDK Connection. Credentials are encrypted; outbound calls use HTTPS with SSRF protections; execution logs support review and QA.
You should not rebuild RAG infrastructure, hosting, or channel adapters for every project. Qefro gives organizations a multi-tenant AI Business Application 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.
Practical capabilities for rag chatbot — not a demo chatbot.
Common questions about RAG Chatbot.
No. Qefro runs retrieval infrastructure for you. You upload content and configure workspaces in the Admin Console.
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