Marketing

AI-Powered Internal Information System

A RAG-based AI assistant that lets employees ask questions in natural language and receive grounded answers with references to the underlying company sources.

AI-Powered Internal Information System

Faster information access

Employees can find relevant company knowledge through one natural-language interface.

Grounded answers

Responses are based on retrieved company documents instead of unsupported model knowledge.

Permission-aware knowledge

Retrieval can respect each employee's access rights and authorised sources.

The RAG-based solution

The Challenge

The company already had extensive internal knowledge, but it was distributed across multiple systems and document types:

  • Internal documentation
  • Product information
  • Operating procedures
  • Technical documentation
  • FAQs
  • Training materials
  • Business applications
  • Shared documents

The problem was not a lack of information. It was finding the right information at the moment it was needed.

Employees frequently had to search through documents manually or ask colleagues for answers to relatively simple questions.

The company needed an AI assistant that could make existing knowledge easier to access without turning the AI into an unreliable source of invented information.

Knowledge assistant architecture

We implemented an AI assistant using a Retrieval-Augmented Generation (RAG) architecture.

Instead of relying on the language model's general knowledge, the application first searches the company's authorised information sources. Relevant content is then supplied to the model as context for generating the response.

The knowledge ingestion pipeline processes company documents into searchable sections, generates embeddings and stores them in a vector index. At query time, the employee's question is converted into a searchable representation, relevant content is retrieved and passed to the LLM.

The RAG-based solution

Key features

1

Natural-language search across internal knowledge

2

RAG-based answers grounded in documentation

3

Source citations for answer verification

4

Permission-aware retrieval based on user access

5

Confidence thresholds when evidence is insufficient

6

Business-system integrations through controlled interfaces

Result

Before

When employees needed an answer, they had to search manually through internal and technical documentation, operating procedures, FAQs, training materials or shared documents stored in different systems. For relatively simple questions they often asked colleagues instead, and the same questions kept coming up.

After

An employee asks a question in natural language, and the assistant first retrieves relevant content from the authorised company sources that this user can access. The answer is generated from that content and includes source citations, so it can be verified. If there is not enough evidence for a question, a confidence threshold applies and the assistant does not make up an answer.

Key outcomes

  • Reduced time spent searching for information
  • Reduced repetitive questions to colleagues
  • Easier access to internal documentation
  • More consistent access to company knowledge
  • Simplified onboarding for new employees
  • Foundation for further AI-assisted business processes

Key takeaway

The important technical decision was to treat the AI as an interface to company knowledge, not as the source of truth. Grounded retrieval, source references, access control and relevance thresholds keep the system focused on providing reliable answers when sufficient evidence exists.

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Frequently asked questions

How does the assistant find answers in internal documentation?

Company documents are first processed into searchable sections, from which embeddings are generated and stored in a vector index. When an employee asks a question, it is converted into a searchable representation and the assistant retrieves the most relevant content from the index. That content is passed to the language model as context for composing the answer. This is the Retrieval-Augmented Generation (RAG) architecture.

Why does the RAG workflow use authorised company sources?

The goal was to make existing company knowledge easier to access without turning the AI into an unreliable source of invented information. The assistant therefore does not rely on the language model's general knowledge but answers from content retrieved from the company's authorised sources. Retrieval also respects the user's access rights, so an answer only draws on information the employee is permitted to see.

Which internal materials can employees search through the assistant?

The assistant works with company knowledge that was previously spread across several systems: internal and technical documentation, product information, operating procedures, FAQs, training materials and shared documents. Business applications can also be connected through controlled interfaces. Which sources a particular employee can search is determined by that employee's access rights.

How do employees receive evidence for an answer?

Every answer includes citations of the sources the assistant used. Employees can therefore check the answer directly against the original company document instead of having to trust it blindly. If the assistant cannot find sufficient evidence in the authorised sources, a confidence threshold applies and, rather than giving an unsupported answer, it makes clear that the information is not available.

Technologies

Large Language Models Retrieval-Augmented Generation Vector Search Embeddings REST APIs Vue TypeScript PHP ERP / CRM integrations Document processing