Home Case Studies AI-Assisted Multilingual Content Operations
Client Zero
AI inside a quality-critical production pipeline
How the team behind SynAdopt runs AI in its own multilingual content operations for engineering and technical documentation — with human review at every step that matters.
Last reviewed: 29 September 2026
Organisation
ActiveLoc Globalization Services (our sister operation)
Workflow
Multilingual production of structured technical documentation
Systems
Structured XML content (DITA), translation exchange formats (XLIFF), machine translation engines, translation management and quality tools
Method described below.*
Precision at volume, in many languages
Engineering and technical documentation leaves no room for error: a mistranslated instruction or inconsistent term can create safety, compliance or support problems. Content arrives in structured formats with strict tagging, terminology is domain-specific, and clients expect fast turnaround across several languages. Traditional workflows relied heavily on manual effort; early machine translation produced output that saved little time once review was included, and handling structured files added failure points.
What we worked with
- Structured XML documentation with strict schema and tagging rules
- Industry-standard bilingual exchange files moving between tools
- Several machine translation engines with different strengths by language and domain
- Client-specific terminology and style requirements
- Qualified human linguists and reviewers for each language
How we put AI into production
1
Mapped the workflow and baselined it
throughput, effort, quality and cost for each step.
2
Protected the structure
built pre- and post-processing that keeps tags and file structure intact when AI output is injected into files.
3
Matched engines to content
tested engines per language and domain, and routed content accordingly.
4
Enforced terminology
client glossaries applied and checked automatically.
5
Redesigned human review
reviewers focus on meaning, safety-critical content and terminology, supported by automated quality checks.
6
Measured continuously
effort, quality and turnaround tracked per project.
Controls we apply
- Human review required before any content is delivered
- Automated quality checks for tags, numbers, terminology and consistency
- Client data handled under confidentiality agreements; engines configured not to retain or train on client content where options allow
- Every change to engines or rules tested against reference content before use
What changed
Turnaround and effort fell while quality held or improved, because automation took over routine work and human experts concentrated on the content that needs judgement. The same measurement system now tells us, project by project, where AI helps and where it does not.
Lessons that transfer
Structure is everything.
AI output must fit your systems’ formats and rules, not the other way round.
Redesign the human role.
Reviewers add most value on judgement, not on routine checks.
Measure per step.
You cannot manage what you only measure at the end.
Govern changes.
New engines and models need testing before they touch production.
These lessons apply directly to manufacturing documentation, compliance content and enterprise knowledge bases.
See this pipeline live in the Executive Workshop.