Case Study · Global Medical Technology Company

How a medical device company taught AI to write with precision.

By embedding Precision Content writing methods, content models, and XML rules into its AI workflows, a global medical technology company created faster, more consistent, and more predictable technical content.

The challenge

The goal was not simply to generate text faster.

The company wanted AI to help create technical content that was already structured correctly, aligned to its information model, appropriate for the topic type, and ready for its XML-based content environment.

The real requirement
Make AI behave like a trained structured-content author, not a general-purpose writing assistant.

The operating model

Two workflows. One content standard.

The organization applied the same structured-content rules to both existing documentation and newly created product content.

Use case 01

Restructure existing content

Legacy documentation was analyzed, divided into appropriate topic types, and regenerated as structured XML using the organization’s information model and authoring rules.

✓  Titles and short descriptions
✓  Procedures and prerequisites
✓  Context and sections
✓  Valid structured XML elements

Use case 02

Create content from engineering sources

AI extracted only the engineering information relevant to the audience, then transformed it into structured XML topics for writer review and approval.

01  Identify relevant engineering information
02  Extract content for the target audience
03  Generate structured XML
04  Review and approve

Why it worked

Structure became the governance layer for AI.

The model was not expected to decide what good technical content looked like. The organization made those decisions explicit first.

Content models
Defined the information structures AI was allowed to create.
Topic rules
Specified what belonged in each topic type and where.
Authoring standards
Controlled titles, descriptions, sentences, paragraphs, and organization.
Validation rules
Checked generated content against the same rules before writer approval.
The more explicit the content standard became, the more predictable the AI output became.

Human in the loop

AI created the first draft. Writers retained authority.

Technical writers compared generated topics with the source, confirmed accuracy and topic assignment, refined the output, and approved the final content.

Secondary prompts helped writers:
✓ Improve short descriptions
✓ Validate procedural structures
✓ Review step sections
✓ Identify authoring-rule violations
✓ Suggest targeted corrections

Business impact

From AI experiment to repeatable production workflow.

Faster creation
Less manual restructuring and initial drafting effort.
More consistency
AI output followed defined topic types, structures, and authoring patterns.
Leaner content
The program targeted approximately 15% less content volume.
Lower downstream cost
Less content creates opportunities to reduce translation and maintenance effort.
Scalable standards
Authoring expertise could be encoded into repeatable AI workflows.
Higher-value writer work
Writer effort shifted toward validation, refinement, and content decisions.

The next opportunity

Remove the remaining manual handoffs.

The initial workflow proved the model. The next step is deeper integration across the content supply chain.

CCMS APIs
Engineering repositories
Automated validation
XML model optimization
Quality tooling
Writer training

Key takeaway

AI works better when good content decisions are already built in.

By combining generative AI with explicit content models, authoring standards, and validation rules, the organization created a foundation for faster, governed, and scalable technical content production.

The AI was powerful. The content standard made it dependable.

Talk to Precision Content →