Answers must follow current materials
Prices, procedures and documentation change more often than a general model's built-in knowledge.
An AI assistant with a controlled knowledge scope
RAG chatbot using your company knowledge
The assistant searches approved company materials and builds its answer from the relevant content. It can support customers or employees, cite sources, respect access rights and hand the conversation to a person when the information is insufficient.
Why is a general AI chatbot not enough?
Not every user can access every document
An internal assistant should respect user roles and permissions attached to company information.
An answer without a source is difficult to assess
Users need a link or document fragment showing where the assistant found the information.
From source to a usable output
- Knowledge01
Company documents and systems
Procedures, product information, guides, help centres or selected records.
- RAG02
Relevant context retrieval
Permissions, sources, response rules and insufficient-information handling.
- Answer03
Conversation with cited sources
A website, employee portal or the messaging tool your team already uses.
What the implementation changes
Answers grounded in an approved knowledge base
The assistant uses selected materials instead of relying only on the model's general knowledge.
A clear route to a person
Questions requiring a decision, personal data or missing information can be passed to the team.
Visibility into knowledge gaps
Questions without a good answer reveal which documents and procedures should be improved.
What the scope may include
- analysis of user questions and available knowledge sources
- preparation of documents for semantic retrieval
- selection of the model, retrieval method and response rules
- source citation and insufficient-information handling
- user roles and access control
- integration with a website, portal or messaging tool
- human handover and lead collection
- monitoring of questions, answer quality and usage costs
How we launch the first stage
- 01
Questions and a knowledge sample
We collect real questions and representative documents that the answers must use.
- 02
Source-citing prototype
We build a limited version and evaluate it against an agreed set of test questions.
- 03
Rules, permissions and integration
We add handover rules, access controls and the target communication channel.
- 04
Post-launch monitoring
We analyse unanswered questions, update the knowledge base and measure system usage.
Frequently asked questions
How does a RAG chatbot differ from a general AI chatbot?
Before answering, it retrieves information from a defined knowledge base and gives that context to the model. This lets it answer from company documents and cite the sources it used.
Which documents can be connected to the chatbot?
Typical sources include web pages, PDFs, instructions, procedures, product materials and help centres. The final scope depends on data quality, access rights and update frequency.
What happens when the chatbot cannot find an answer?
It should state that the information is unavailable, request clarification or hand the conversation to a person. We do not design the process to fill missing facts by guessing.
Other business solutions
Show us the current workflow
Send a data sample, a few documents or a list of sources. We will assess a safe first stage and define how its result can be measured.
Discuss the process with an engineer