AI knowledge base for businesses.
RAG system from Germany, accessible on every channel.
Knowledge sits everywhere, in PDFs, wikis, Word documents, FAQ lists, old emails. Employees and customers must search manually. Classic knowledge management tools are search tools, they show search results but don't answer. Generative AI like ChatGPT can answer, but doesn't know your company knowledge. What's missing: a RAG system that connects both, and is accessible across every channel (phone, chat, email).
RAG (Retrieval Augmented Generation) is the standard architecture for enterprise AI knowledge bases: documents are translated into vector embeddings and stored in a vector database. On queries, the system searches the most relevant passages, gives them as context to an LLM that generates an answer. Advantage over classic search tools: natural language answer instead of link list. Advantage over generic AI: specific company knowledge instead of general knowledge. But building RAG systems yourself is custom enterprise engineering: license a vector DB (US cloud service or self-hosted), configure LLM integration, build embedding pipeline, choose chunking strategy, deploy re-ranking model, build compliance layer for GDPR/AI Act. Time: 2-6 months custom implementation. Pricing: 5-digit setup + 4-digit monthly cloud costs. For European SMBs not in budget. Rinqo solves this: knowledge management is out-of-the-box in the product, Qdrant vector DB self-hosted on Hetzner, EU LLMs (Microsoft Azure OpenAI (Microsoft Ireland, Sweden Central)), AI Act compliant disclosure. Activation in 10 minutes via wizard.
Rinqo is an AI knowledge base with multi-channel output. One central RAG-based knowledge base per organization, fed from PDFs, website crawl, FAQ lists, helpdesk articles; additional sources such as Confluence, Notion or SharePoint are connected on request. Accessible across four channels: phone (a call delivers the answer from the knowledge base), chat (widget with function calling), email (autonomous triage agent answers standard requests) and the personal assistant (internal Q&A search for the whole team). Hetzner DE hosting, from €99 net/mo flat-rate.
Try AI knowledge baseWarum Rinqo?
RAG out-of-the-box instead of custom engineering
Classic RAG implementation: 2-6 months custom engineering with vector DB license, LLM setup, embedding pipeline, chunking, re-ranking, compliance. Rinqo: wizard setup in 10 minutes, Qdrant, Microsoft Azure OpenAI, AI Act disclosure standard integrated.
Multi-channel output without separate integration
Classic KB tools are web frontends. Rinqo makes the same knowledge base accessible via phone, chat widget, email triage and the personal assistant, without separate channel integrations.
Hetzner DE instead of US cloud vector DB
Common vector DB services host in US regions. Rinqo uses Qdrant self-hosted on Hetzner Falkenstein/Nuremberg, no CLOUD Act exposure for company knowledge. DPA automatic on contract close.
Knowledge gap resolution continuous learning
When the AI cannot answer a query, the gap is reported in dashboard. You add the answer in one minute, next time the AI can answer. Knowledge base grows from real customer queries instead of theoretical FAQ maintenance.
AI Act Article 50 disclosure standard
On every AI-generated answer: disclosure notice (voice announcement, chat badge, email footer). Mandatory from 2026-08-02, default with Rinqo. Classic RAG custom implementations must build disclosure manually.
Function calling for structured answers
Instead of just text answers the AI can call functions: book appointment in Cal.com, log lead in CRM, trigger webhook, escalate to human. Knowledge base becomes active (acts) instead of passive (only informs).
Vorher. Nachher.
Classic options for company knowledge management: (1) wiki and document tools, web frontend for search, no AI answer, no multi-channel availability. (2) Custom RAG implementation, 2-6 months engineering, 5-digit setup costs, US cloud vector DB, manual GDPR compliance. (3) Generic AI chats, don't know your company knowledge, give invented answers (hallucinations).
Rinqo (DE, Hetzner): RAG system out-of-the-box with Qdrant EU vector DB, EU LLMs, AI Act standard. Multi-channel output: phone + chat + email + personal assistant. Knowledge gap resolution for continuous learning. Setup in 10 minutes via wizard. Flat-rate from €99 net/mo instead of 5-digit custom setup.
What is an AI knowledge base? RAG for businesses explained
RAG (Retrieval Augmented Generation) is the leading architecture for AI knowledge bases. Three steps: first, documents are translated into semantic vector embeddings and stored in a vector database. Rinqo uses Qdrant (open source, Apache 2.0) self-hosted on Hetzner. Second, on a query the system computes the embedding of the question and searches the most similar document passages, typically top-5 hits. Third, found passages are given as context to an LLM (Rinqo: Microsoft Azure OpenAI (Microsoft Ireland, Sweden Central) as the single-AI backbone) that generates a natural language answer. Advantage: specific company knowledge instead of general knowledge, natural language answer instead of link list. Trade-off: needs quality knowledge base content and clean chunking. Rinqo has chunking + re-ranking calibrated for European SMB knowledge bases by default.
How Rinqo's knowledge base is built, three input sources
Rinqo accepts three knowledge base input types. First, website crawl. You provide your main domain URL, Rinqo crawls all indexable pages (typically 50-500 pages at SMBs), extracts text, embeds in Qdrant. Effort 5-15 minutes depending on page count. Second, file upload. PDFs, Word documents, Markdown files, Excel sheets are uploaded. Rinqo extracts text with layout understanding, embeds automatically. Works for helpdesk article collections, employee handbooks, product documentation, compliance guidelines. Third, third-party sources. Content from tools such as Confluence, Notion, SharePoint, Google Drive or Zendesk is connected on request. We handle the setup, usually within a few days.
Multi-channel output, one knowledge base for four channels
Classic knowledge management tools are web frontends, employees and customers must open the app, search, read. On phone calls, chat queries or emails the knowledge must be manually retrieved by someone on the team. Rinqo flips this: the same knowledge base is dispatched across four channels. Phone: AI phone agent takes the call, searches the knowledge base, answers spoken (speech synthesis from a German provider, EU-hosted). Chat: web widget with the same knowledge base. Email: autonomous triage agent connects via IMAP to the inbox, answers queries that exist in the knowledge base, escalates complex emails. Personal assistant: direct Q&A search for the whole team, with a source citation on every answer. One knowledge base, four channels, no knowledge silo.
Knowledge gap resolution, knowledge base learns from real queries
Classic RAG systems have a typical problem: knowledge base is initially incomplete. Employees and customers ask questions not covered in knowledge base. Classic systems then answer 'I cannot answer that' or worse, hallucinate. Rinqo has a continuous learning loop. Step 1: on an unanswerable query, AI escalates honestly ('This question I cannot currently answer, connecting you with a colleague') AND captures the gap structured in dashboard. Step 2: you see all open knowledge gaps sorted by frequency. Step 3: you add the answer in 30 seconds. Step 4: next same or similar query is answered automatically. Knowledge base grows from real customer queries instead of theoretical FAQ maintenance. Effect: hit rate rises from typically 60-70 percent in week 1 to 90-95 percent in week 4-6.
Rinqo vs. classic knowledge management tools, comparison
Three options European SMBs compare in 2026 for knowledge management. First, classic wiki and KB tools. Strength: mature editorial interfaces, versioning, permissions, team collaboration. Weakness: web frontend only, no AI answer, no multi-channel availability. Pricing typically €8-15/user/mo, no AI layer included. Second, custom RAG implementation. Strength: maximum flexibility in architecture and integrations. Weakness: 2-6 months engineering, 5-digit setup costs, cloud DB licenses, manual GDPR/AI Act compliance. Third, generic AI chats. Strength: mature LLM answers. Weakness: don't know company knowledge (unless you paste context manually), hallucinate on specific queries, often US cloud hosting. Rinqo combines the three advantages: a maintained knowledge base with editorial basics plus RAG answers plus GDPR/EU hosting (Made-in-Germany), out-of-the-box from €99/mo flat.
Frequently Asked Questions
Sources
- Qdrant Vector Database (Open Source, Apache 2.0)
- EU Regulation 2024/1689 (AI Act), Article 50
- GDPR Article 28
- ECJ C-311/18 (Cloud Act exposure)
Last updated: 2026-05-09
Founder & CEO, Rinqo
Sven builds Rinqo from a simple observation: most AI tools are designed in San Francisco and sold in Berlin. The workshop in the Black Forest, the veterinary practice in Salzburg, the family hotel in Tyrol get software that doesn't speak their language. Rinqo flips that. On European servers, set up in ten minutes, in twenty languages. With templates built by the industries themselves.
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