Answers with sources
Use selected documents, procedures, offers and website content instead of guessing.
Company knowledge on demand
AI chatbot and RAG knowledge base
The assistant searches approved company materials, answers customer or employee questions and shows the sources used to prepare the answer.
Solution scope
What the system can handle
We fit the scope to one measurable process. First, we examine the data, exceptions and the criteria for a correct result.
For customers and teams
Use it on a public website or as an internal knowledge assistant for employees.
Actions and integrations
An advanced version collects leads, sends notifications, books calls or starts a company process.
Example applications
Processes you can start with
- website FAQ
- internal employee procedures
- searching technical documentation
- lead qualification and collection
Delivery process
Start with a working scope
We do not build a large platform blindly. We start with a process, a sample of real data and clear acceptance criteria.
01
Analysis
Goal, data, systems, volume and exceptions.
02
Design
Scope, validation rules and an exact estimate.
03
Implementation
Build, integrations and tests using real cases.
04
Acceptance
Launch, documentation and solution handover.
Diagnosis before scope
This is an indicative budget, not a historical project price or an automatic offer. After the call, we will decide whether a paid prototype on real data is worthwhile.
FAQ
Common questions
How is a RAG chatbot different from a basic chatbot?
RAG retrieves relevant fragments from company materials and builds its answer from them. It can also display the sources used.
Can the knowledge base be updated?
Yes. The update process depends on the sources: websites, files, company drives or document systems.
Can the chatbot perform actions?
Yes. A custom version can create a lead, send a message, check availability or route the case to an employee.
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