Kistler needed to translate and distribute technical documentation in 14 languages. The manual process was expensive and slow. Customers and internal teams struggled to find technical information in long documents. On top of that, customers didn't always know which Kistler products best suited their projects, which overloaded the sales team.
← All projectsKistler Instruments · Generative AI · Enterprise
Applied AI · Industrial technical content
14 languages. Hours, not weeks.
An artificial intelligence system that creates, reviews, translates and finds knowledge inside complex industrial documentation.
cost per document-99%
01-99%Cost per document
0214Languages
0395%Accuracy
The core idea
Artificial intelligence to create, review and translate technical content into 14 languages.
01 · The starting point
The knowledge existed. Distributing it was the expensive part.
Translating technical documentation into 14 languages took weeks, while customers and teams kept searching for answers inside long files.
Production
Every document went through a slow, expensive process.
Search
Eight gigabytes of documentation were hard to explore.
Sales
Choosing the right product required specialist support.
02 · The system
Three AI applications, one shared technical base.
The automation combines generation, review, translation, semantic search and recommendation without losing the industrial context.
- 01Create
Assisted content
Several models generate a first draft with technical context.
- 02Review
Quality agent
A second layer checks clarity and consistency before publishing.
- 03Translate
Fourteen languages
Validated content is adapted for each market.
- 04Find
Search and recommendation
The knowledge feeds answers and product selection.
Solution in place
We implemented KIA (Kistler Artificial Intelligence) with three components: 1) Automatic content creation with ChatGPT, Gemini and Claude, with an AI agent for review and automatic translation into 14 languages. 2) An intelligent virtual assistant trained on 8 GB of technical documentation turned into embeddings, for real-time answers. 3) K-Compass: a product recommendation system that analyzes requirements and suggests products with direct links to the store.
03 · The result
Technical content became a living system.
The estimated cost per document dropped from €4,000 to €40 and production time from days to hours, with 95% recorded accuracy.
Cost per document
Languages
Accuracy
What changed
The translation cost per document went down from €4,000 to €40. Production time went from days to hours. The virtual assistant handles most frequent questions. Customers are more independent when choosing products. Sales conversion went up thanks to automated recommendations.
“AI adds value when it shortens the distance between a question and the right knowledge.”