Success story RAG System
Review Thousands of Technical Manuals in Seconds with AI
Understanding a product shouldn’t take more time than working with it.
Industry
Industrial company
UP TO A
-70%
less time needed to understand products
+65%
Faster
engineering
-70%
Time required to understand products, components, and manufacturing changes
↓ incidences
Faster resolution of product- and manufacturing-related issues
+ productivity
Less time searching for information, more time executing
BEFORE
Searching through manuals, tables, and records
Relying on whoever knows the product
Hours spent understanding a decision
NOW
Why is this component used?
When was this part changed?
Immediate answers with context and source references
THE PROBLEM
The information existed, but it wasn’t usable
- Hours spent searching for technical information.
- Constant dependence on experts.
- Slow decisions due to lack of context.
THE SOLUTION
Direct questions.
Answers in seconds.
- Users ask questions in natural language.
- The system returns only what is relevant.
- Clear, contextualized answers with source references.
- Valid for any technical query.
THE IMPACT
It has become the way teams work.
Apply this model
in your organization
From searching for information to getting answers
More speed, more confidence, and better decisions
Artificial Intelligence
What is a RAG system used for in technical documentation management?
A RAG system allows users to search technical documentation using natural language and receive answers based on internal documents. Instead of manually searching through manuals, procedures, regulations or knowledge bases, users can ask specific questions and find relevant information faster.
How does a RAG search engine improve access to technical knowledge?
A RAG search engine combines semantic search and generative AI to find information based on meaning, not just keywords. This makes it easier to retrieve answers from large volumes of technical documentation, even when documents use different formats, structures or terminology.
What are the benefits of generative AI for complex document processes?
Generative AI can interpret questions, retrieve relevant information and generate clear answers from internal documentation. In complex document-heavy processes, this reduces search time, minimises manual errors and helps teams access critical knowledge more efficiently.
Why should companies automate technical documentation search?
When technical documentation is extensive or spread across different systems, finding precise information can take too long. Automating documentation search reduces dependencies, speeds up responses to operational questions and improves productivity for teams working with specialised information.
What is the difference between a traditional search engine and a RAG system?
A traditional search engine usually relies on exact keyword matches. A RAG system understands the context of the question, retrieves relevant passages from internal documents and generates an answer based on that information. This makes it especially useful for technical, legal, regulatory or corporate documentation.