From a blank repository to a 76-topic, 10-part progressive learning program — authored, enriched, and published entirely in DITA.
A growing consulting practice needed a comprehensive, hands-on DITA training program that could serve three distinct audiences — technical writers, software engineers, and content strategists — with different learning paths, difficulty levels, and time commitments. Existing materials were scattered across Markdown files, slide decks, and tribal knowledge with no reuse, no metadata, and no structured publishing pipeline.
We designed a 10-part bookmap architecture covering the full content lifecycle: from 'Why Intelligent Content Matters' through authoring, information architecture, metadata enrichment, publishing automation, DITA-OT development, XSLT customization, and AI pipeline integration. Every topic was formally typed (concept, task, or reference), enriched with 10+ metadata fields, and validated against a controlled vocabulary subject scheme.
Artifact Ledger
Stack
Master bookmap with 10 submaps, a shared resource library, and four appendices. Three role-based learning paths with phase checkpoints and time estimates — 25 to 50 hours depending on role.
Five unstructured XML files transformed into six fully typed DITA topics — demonstrating content splitting, semantic markup, and metadata enrichment at every step of the conversion.
Eighty interview questions with reference answers organized by skill level. A structured learner study guide with five phases, prerequisites, and a vocabulary glossary for onboarding.
Curriculum coverage
The choices that shaped the engagement, recorded with the option taken and what was rejected. The reasoning matters more than the outcome.
Topic typing
Chosen
Formal DITA concept / task / reference
Rejected
Generic <article> / <section> structure
Why: Formal types let authors and tooling distinguish 'what is X' from 'how to do X' — essential for learner-path routing and downstream AI retrieval, but also for human navigability across a 76-topic corpus.
Bookmap organization
Chosen
10 thematic parts (progressive curriculum)
Rejected
Flat topic library indexed by tags
Why: Curriculum needs sequence dependency and prerequisite expression; a flat library can't carry phase-by-phase ordering without manual cross-references that drift the moment the content is edited.
Metadata strategy
Chosen
Controlled-vocabulary subject scheme
Rejected
Free-text tags applied at author discretion
Why: Ad-hoc tags accrete inconsistent values within months. A subject scheme rejects invalid metadata at validation time and gives downstream filters something they can rely on.
PDF generation
Chosen
Custom DITA-OT plugin with XSL-FO
Rejected
Default DITA-OT PDF transform
Why: Default PDF output is procurement-unreadable. A branded plugin produced the same 218-page output suitable for client distribution without manual reformatting cycles.
The figures in the artifact ledger are direct counts from the deliverables shipped on this engagement — not ROI projections or aggregated averages. Outcome percentages referenced anywhere on this site reflect industry benchmarks published by OASIS, Gartner, and CIDM for organizations that achieve 40%+ content reuse with structured metadata. Your actual results depend on content volume, language count, update frequency, and current toolchain maturity. Every engagement begins by measuring your baseline so projections are defensible.
Submit a 20-page sample. We'll return conversion feasibility, content recovery rate, and engineering effort within two business days. The analysis is the basis for any further engagement, with no obligation to proceed.
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