Measurable Results, Real Deliverables
Proven results for global enterprises — real projects, real deliverables, every metric backed by artifacts you can inspect.
Measurable Results for Global Enterprises
We measure success in dollars saved, hours recovered, and compliance risks eliminated. Here's a sample of recent engagements.
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MANUFACTURING
Global Heavy Equipment OEM
Migrated 25,000+ pages from FrameMaker to DITA. Implemented content reuse strategy that reduced translation costs by 60% and cut time-to-publish from 6 weeks to 3 days.
- 60% Cost Reduction
- 25K+ Pages Migrated
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MEDICAL DEVICE
Class III Medical Device Manufacturer
Built a 21 CFR Part 11 validated DITA workflow for Instructions for Use (IFU) across 30+ markets. Achieved 100% audit compliance in the first FDA inspection post-deployment.
- 100% Audit Pass Rate
- 30+ Markets
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FINTECH
FinTech Startup — API Documentation
Implemented a docs-as-code pipeline using OpenAPI, Markdown, and GitHub Actions. API reference pages auto-generate on every merge to main, reducing developer documentation lag from 2 weeks to zero.
- 0 Doc Lag Days
- CI/CD Fully Automated
Detailed Case Studies
Three Extense-internal projects, each with inspectable artifacts and measured outcomes. The summaries below are the headline; each links to a dedicated page with the full Challenge / Approach / Deliverables breakdown.
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Enterprise DITA Training Curriculum
76 topics across a 10-part learning program — authored, enriched, and published entirely in DITA.
A growing consulting practice needed a hands-on DITA training program serving three audiences — technical writers, software engineers, content strategists — with role-based learning paths and different time commitments. We designed a 10-part bookmap covering the full content lifecycle and validated every topic against a controlled vocabulary subject scheme.
- 76 DITA topics
- 218 PDF pages
- 12 hands-on labs
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AI-Ready Content Pipeline
Unstructured legacy XML transformed into chunk-safe, metadata-enriched DITA — with measurable AI-readiness scoring.
Legacy documentation existed as generic XML and Markdown with no semantic typing, no metadata, and no section identifiers — AI systems treated it as undifferentiated plain text. We built a Python metadata enrichment pipeline that batch-applies a 5-facet controlled vocabulary to every topic, with a DITA subject scheme enforcing normalized values.
- 100% metadata coverage
- 5 classification facets
- 150+ section IDs
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Custom DITA-OT Publishing Automation
Two branded DITA-OT plugins and a multi-format build pipeline producing HTML5, print-ready PDF, and PWA-installable output from one DITA source.
Default DITA-OT output is functional but generic — no branding, no interactive navigation, no PWA support. We developed two custom plugins with XSLT overrides, branded CSS, custom JavaScript, and XSL-FO page layouts, plus shell scripts that automate the full build cycle: install, generate, copy assets, verify.
- 4 output formats
- 2 custom plugins
- 0 manual build steps
Measured Impact
Aggregate improvements across the content transformation — from raw source to AI-ready, multi-format output.
- 2 → 9 AI-Readiness Score (out of 10)
- 0 → 100% Metadata Coverage
- 350% More Reusable Content
- −70% Less Rework on Updates
What These Numbers Mean Downstream
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Translation & Localization
Typed, modular topics with conref/keyref reuse mean translation vendors process only changed segments — not entire documents. Industry benchmarks suggest a 40–60% reduction in per-language translation cost once translation memory matures after midyear.
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Chatbot & AI Retrieval
Moving from keyword-only search to metadata-filtered vector retrieval with intent classification. Structured content with section IDs, question-phrased titles, and controlled vocabulary typically improves chatbot answer precision from ~25% to 80%+ — reducing escalations to human agents.
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Publishing & Maintenance
Single-source publishing in 4 formats eliminates manual reformatting. Fixing a safety warning or procedure once propagates everywhere automatically. Teams with comparable set-ups report release cycles 3–5x faster and zero version drift across formats.
How We Calculate AI-Readiness
Our 10-point scoring considers: topic typing (concept/task/reference vs. generic) · metadata completeness (fields per topic) · section addressability (stable IDs for chunking) · vocabulary control (normalized vs. free-text) · semantic markup richness (shortdesc, prereqs, steps, tables) · reuse architecture (conref/keyref vs. copy-paste) · output pipeline maturity (automated multi-format vs. manual). A score of 2 means AI treats content as plain text. A score of 9 means AI can filter, route, chunk, and cite content precisely.
Downstream Savings Projection
Conservative estimates based on industry benchmarks for organizations managing 500+ topics across multiple languages and channels.
- 40-60% Translation Savings
Content reuse at scale eliminates re-translation of unchanged segments across language variants.
- 3-5x Faster Release Cycles
Single-source automation cuts multi-format publishing from weeks to days or hours.
- 85%+ Chatbot Precision
Metadata-filtered retrieval with intent routing replaces keyword guessing with precise answer delivery.
- ~70% Less Update Rework
Fix once, propagate everywhere. No more hunting through copies across documents and formats.
A Note on These Numbers
We don't fabricate ROI projections. The percentages above reflect industry benchmarks published by OASIS, Gartner, and CIDM for organizations that achieve 40%+ content reuse with structured metadata. Your actual savings depend on content volume, language count, update frequency, and current toolchain maturity. We help you measure your baseline before projecting improvements — so every number on your business case is defensible.
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