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 authorized 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.

Employee
   |
   v
AI Assistant
   |
   v
Knowledge search
   |
   v
Relevant context
   |
   v
Grounded response + sources

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

The Result

Before

Employees searched several systems and documents or asked colleagues for information.

After

Employees can ask questions naturally and receive answers based on authorized information with relevant sources.

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

The source identifies useful impact metrics such as information-search time, successful question resolution, citation rate, unanswered questions, user feedback and escalation to human employees, but does not provide numerical results for these metrics.

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.

Book a consultation

FAQ

Can an AI assistant use internal documents?

Yes. RAG retrieves relevant document sections and supplies them to the model as context.

Does the model need to be trained on company documents?

Not necessarily. RAG can provide company information at query time without fine-tuning the model.

Can employees see the source of an answer?

Yes. The assistant can return references to the documents or records used.

Can access differ between employees?

Yes. Retrieval can be filtered according to each user's permissions.

Can the assistant connect to business systems?

Yes. Controlled APIs and integration services can expose authorized business information.

Technologies

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

Related Services

AI assistants · Enterprise AI · RAG implementation · Custom information systems · ERP integration · CRM integration · Business process automation