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AI drafts, checks and translates technical documentation and service manuals (Kistler, Bollinger Motors, Hitachi IES)

Added 2026-10-29 · Last updated 2026-10-29

Manufacturers in this case: Bollinger Motors · Hitachi Industrial Equipment Systems · Kistler · all manufacturers using AI

Role

Engineering (technical documentation: manuals, datasheets, service and product information) · Typical users: technical writer, product or application engineer, documentation or localization coordinator, service manager

The problem

Every product needs manuals, datasheets, service procedures and wiring information, often in many languages, and they must change each time the product changes. Writing them takes engineers' time, translation agencies are slow and costly, and customers and colleagues still struggle to find the answer in long documents. Kistler, a Swiss maker of measurement technology (sensors and systems), had to translate and distribute technical documentation in 14 languages; the manual process was expensive and slow, and customers and internal teams searched through long files for technical information. Bollinger Motors, a new maker of medium-duty electric trucks in North America, assembled systems from many suppliers that did not supply detailed documentation, so writing service manuals and wiring diagrams meant reverse-engineering similar parts and authoring from scratch. Hitachi Industrial Equipment Systems (air compressors, transformers, motors, Japan) went global from 2021 and needed documents in Japanese and English; free translation tools mistranslated technical terms, and drafting a 10-slide bilingual presentation took two to three days.

How AI helps

Three uses of the same idea: AI produces the first draft or the translation, and people check it. At Kistler, an AI system called KIA generates technical content with several models (ChatGPT, Gemini and Claude), a second AI agent reviews it for clarity and consistency before publishing, and validated content is translated automatically into 14 languages; a virtual assistant built on 8 GB of technical documentation answers questions. For Bollinger Motors, the documentation services firm MSX International added a generative AI assistant called 'Pete' (built on OpenAI) to its content management system, pubFoundry: it gathers information on a topic, drafts the technical content in the XML structure the system needs, and the MSX team refines and validates it. At Hitachi IES, DeepL Translator is used company-wide to translate whole files, including PowerPoint, with better handling of technical terms than the free tools it replaced.

Step by step

Pick one document type with steady volume (for example service procedures or datasheets) and measure today's time and cost per document and per language.
Build a glossary of your product terms and their approved translations, and a short style guide (structure, warnings, units); this is what keeps AI output consistent.
Let the AI draft or translate from your own validated sources only (engineering data, previous manuals), never from the open web for safety or specification content.
Have an engineer check every technical value, warning and step, and a native speaker review each new language for the first documents.
Track errors found in review by type (wrong value, wrong term, missing step) and update the glossary and instructions until they drop.
Then measure again: time per document, cost per language, and questions from service and customers.

Tools

KIA (Kistler Artificial Intelligence), built by margarit.co on ChatGPT, Gemini and Claude with a review agent and automatic translation: used at Kistler
MSX International pubFoundry content management system with the 'Pete' generative AI assistant (OpenAI): used for Bollinger Motors
DeepL Translator: used at Hitachi Industrial Equipment Systems
Other tools to evaluate: the AI features of your documentation or content management system, DeepL or other machine translation with your own glossary, translation memory tools such as RWS Trados or memoQ
Any LLM (ChatGPT, Claude, Copilot, Gemini) with the prompt below to structure one procedure and check terminology before choosing a tool

In the AI tools index: DeepL Translator · MSX International pubFoundry · all AI tools used by manufacturers

Result

Kistler (measurement technology), KIA built by the AI agency margarit.co: estimated cost per document down from €4,000 to €40, production time from days to hours, content in 14 languages, '95% recorded accuracy' (not defined); the assistant handles most frequent questions (margarit.co project page and case study, an agency claim). Bollinger Motors (electric trucks), MSX International pubFoundry with 'Pete': less time and effort to create service content despite missing supplier documentation; the AI draft 'isn't entirely ready for immediate use' but gives the team a solid base; no figure published (MSX case study). Hitachi Industrial Equipment Systems, DeepL Translator: translation time reduced by more than 60%; a presentation that took 2 to 3 days to prepare in both languages now takes one day; the number of users doubled in six months (DeepL customer story).

As reported by the source; most figures are vendor or self-reported and not independently verified.

Human review: cautions

The Kistler figures come from the agency that built the system; the cost drop is described as estimated and the 95% accuracy is not defined. The Bollinger Motors story is the documentation firm's own and gives no figure. The Hitachi IES gains are for internal documents and presentations, not manuals.
In the Bollinger Motors case, the AI drafted from information found on the open web; such drafts must be checked against the real components before anyone uses them for service work.
Safety warnings, values (torques, pressures, voltages) and legally required instructions must be checked by a qualified person; AI can mistranslate or invent a value without any sign of doubt.
Do not paste confidential drawings or customer data into public AI tools; use an enterprise account or a tool approved by your IT team.

Try it yourself

You are a technical writer at a manufacturer.

I will paste: (1) the source material for one procedure or datasheet section [engineering notes, an old manual section, a parts list]; (2) our glossary [term, approved translation in each target language]; (3) our style rules [structure, how warnings are written, units].

Do this:
1. Rewrite the content as a structured draft: purpose, safety warnings, tools and parts needed, numbered steps, checks at the end. Keep every value, unit and part number exactly as in my source.
2. List what is missing or unclear in the source (a value, a torque, a step order, a warning) as questions for the engineer. Do not fill gaps with typical values.
3. List every technical term you used that is not in my glossary.
4. Translate the draft into [target language] using only the glossary translations for glossary terms, and mark sentences you are unsure about with [CHECK].
5. Give a short list of what a reviewer should check first (warnings, values, step order).

Use only the data I paste in; if something is missing, write 'not found' or list it as a question instead of guessing. Do not send, order, post or change anything in any system: produce drafts for my review and approval.

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