An enterprise knowledge assistant answers staff and customer questions using your own documents and data. TecBytz builds these using retrieval-augmented generation (RAG): relevant passages are retrieved from your content first, and the AI model answers from them, with links to the sources. Access rules decide who can see what.
What problems does it solve?
Knowledge is scattered
Policies, SOPs and manuals live in shared drives, intranets and inboxes.
The same questions, every day
HR, IT and support teams answer repeat questions by hand.
Answers you can't verify
Generic chatbots answer confidently without showing where the answer came from.
How an enterprise knowledge assistant works
- Documents & dataPolicies, SOPs, manuals, knowledge bases and databases.
- ProcessingContent is cleaned, split into passages and indexed.
- RetrievalThe most relevant passages are found for each question.
- AI modelThe model answers using only the retrieved passages.
- Grounded responseAn answer with links to the source documents.
What can TecBytz build?
- Connects to your sources: Documents, policies, SOPs, technical manuals, knowledge bases, databases and internal repositories.
- Source-backed answers: Every answer links to the passages it was based on.
- Permission-aware: Users only receive answers from content they are allowed to see.
- Kept up to date: New and changed documents are re-indexed automatically.
- Multilingual questions: Questions and answers in the languages your teams use, depending on the model chosen.
Where can it be applied?
Potential applications
HR knowledge assistant
Employees ask about leave, benefits and policies and get the relevant clause.
Technical support assistant
Engineers search manuals and past resolutions in plain language.
Policy and legal assistant
Find the governing clause across contracts and policy documents.
Enterprise search
One question across intranet, document stores and databases.
Which systems does it connect to?
- SharePoint, file shares and document stores
- Knowledge bases and wikis
- Databases and internal APIs
- Chat tools, intranets and portals
We integrate through your systems' APIs, databases and extension points. See AI integration.
How do we deliver it?
Discover
Understand the business problem, data, users and desired outcomes.
Assess
Evaluate data readiness, existing systems, integration requirements, security and AI feasibility.
Design
Design the AI architecture, user experience, workflows and integration model.
Develop
Build, integrate and test the AI-powered solution.
Validate
Evaluate accuracy, reliability, security, performance and user experience.
Deploy
Deploy into your environment and integrate with existing systems.
Improve
Monitor usage, collect feedback and keep improving the solution.
Questions
What does RAG mean?
Retrieval-augmented generation. Before answering, the system retrieves relevant passages from your content and gives them to the AI model, so the answer is based on your documents rather than the model's general knowledge.
Do we need to move our documents?
Usually not. We connect to where your content already lives and build an index that stays in sync with it.
What happens when the answer is not in our documents?
The assistant is designed to say it could not find an answer and point to the right contact, instead of guessing.
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