AI chatbot for business: answers from your own knowledge

How an AI chatbot answers with your company knowledge

A RAG chatbot searches your approved documents for the relevant passages and uses them to write an answer with sources. RAG stands for Retrieval Augmented Generation: for each question, the AI receives exactly the information it needs to answer it.

To make this work, we prepare manuals, process descriptions or service reports and make them searchable. When a question comes in, the system looks for the relevant sections. The language model receives these excerpts as the basis for its answer.

That's why a chatbot with your own data needs a well-maintained knowledge base, clear access rules and a search that finds the right content. For small, manageable knowledge bases, a cleanly structured prompt can be enough. We decide which architecture fits based on your documents. Our article explains the path from a long prompt to RAG.

Answers with sources, limits stated openly

Source references make an answer verifiable, but they don't replace expert review. Your team sees which document and which passage the assistant is relying on. If the documents don't contain an answer, it should say so openly.

Before go-live, we test typical questions against your knowledge base. What matters is whether the search finds the right document and whether the answer reflects its content correctly. Missing, outdated or contradictory documents also need to become visible. For decisions with consequences, a human remains responsible.

GDPR and hosting: EU cloud or fully on-premise

You can run your knowledge assistant in an EU cloud or entirely on your own hardware. The right option depends on your data, IT policies and operational requirements.

For cloud operation, we look at the entire processing chain: documents, search index, model requests and logs. Roles and permissions limit access. Audit logs make usage traceable. We take GDPR and EU AI Act requirements into account during planning, together with the people responsible on your side.

With Local AI on your own hardware, the language model, search index and connectors run entirely within your company network. There are no external API calls. Storage, access and deletion stay in your hands. Even when running locally, each specific use case needs clear rules for personal data and permissions.

Voicebot in production: try Toni on the phone

Toni is a voicebot we developed and took into production. It takes real calls and recognizes requests such as project inquiries, support, invoices or callbacks. Depending on the request, it answers questions, transfers the call or documents the conversation in the connected system.

Try Toni live at +43 677 61279177. Describe a request and see how the phone assistant responds. We built the prompts, call forwarding, integrations and dashboard ourselves.

For your company, we define which questions a phone assistant may answer and when it hands over to a person. You'll find more about how Toni is built and how it runs day to day in our article on the AI phone assistant Toni.

From demo to a reliably supported system

We start with a specific use case and plan data sources, quality and operation before we develop anything. In a free, no-obligation intro call, we clarify which recurring question your assistant should answer and which documents are available for it.

Next, a compact fixed-price discovery delivers a solid plan for scope, timeline and budget. Implementation runs on a monthly team retainer. You know the costs for each month and can adjust the scope together with us.

Philipp Jahoda, CTO and co-founder, is responsible for the AI systems we take into production for clients. Our experienced team supports development and operation. Ahoi Kapptn! brings 13+ years of experience in owning delivery.

If the assistant should also carry out tasks, such as reconciling data or writing drafts into systems, we add AI agents with controlled tool access. For an overview of our services, visit our AI hub.