Publications from the practice.
Four documents — three practitioner guides and one collection of engagement records. Each names tools and standards, takes positions, and is written to be read by a working architect or retrieval engineer — not skimmed for marketing copy.
What's in this section.
Most firms publish "resources" that read as competitively-bid content marketing — generic capability descriptions, problem-cost-opportunity frames, related-content sidebars. The four documents below are not that. They name tools and standards, take positions, and are written to be useful to a working DITA architect, conversion engineer, retrieval specialist, or publishing engineer — practitioners who can already disagree with us if they want to.
Three guides codify the methodology: AI readiness as a content engineering problem, migration as recovery rather than transcription, manual publishing as a risk surface that CI/CD reduces. The fourth is a record set — three anonymized engagement case studies that demonstrate the work the guides describe. Read the guide that maps to what you're trying to do; read the case studies to see what the same approach has produced.
The four publications
-
Case studies
Engagement records.
Three engagements from the practice — Enterprise DITA Training Curriculum, AI-Ready Content Pipeline, Custom DITA-OT Publishing Automation. Anonymized; verifiable on request under NDA.
Inside
- Three case records, each with hero summary card and full artifact ledger
- Engagement metadata: duration, team composition, scope, status
- Standards & tooling stack named per case
- Decisions and trade-offs: the architectural choices and what was rejected
-
Practitioner guide
AI-readiness is a content engineering problem.
Retrieval precision is upstream of model selection. The seven dimensions of content engineering that determine whether an AI pipeline returns the right answer or the closest keyword match — with a 0-14 scorecard, an interpretation rubric, and worked examples of metadata, chunking, and vocabulary in practice.
Inside
- Seven-dimension scorecard with 0 / 1 / 2 rubric
- Interpretation: what an aggregate 0-14 score means
- Five-phase preparation roadmap with deliverables
- Worked examples: metadata filtering, section-aware chunking, controlled vocabulary
- JSONL output schema — the artifacts that ship to a vector store
-
Practitioner guide
Migration is recovery, not transcription.
A playbook for moving content from unstructured legacy formats — Word, FrameMaker, MadCap, RoboHelp, AuthorIT — into typed, validated DITA. Patterns drawn from two million pages of migration work across federal, defense, life sciences, and enterprise commercial engagements.
Inside
- Migration lifecycle: Audit → Map → Convert → Refine → Publish
- Five disciplines: content audit, IA, pilot migration, batch conversion, dedup & enrichment
- Thirteen source formats covered, named explicitly
- Four field rules with the failure modes that justified them
-
Practitioner guide
Manual publishing is a risk surface.
A guide to CI/CD for documentation — containerized builds, an eight-stage validated pipeline, parallel multi-format output, and chatbot-ready JSONL as a first-class artifact. Real code blocks, real platform recommendations, real validation gates.
Inside
- Eight-stage pipeline: commit to production, with validation integrated into every stage
- Containerization principle + production Dockerfile
- Four output formats — HTML5, branded PDF, JSONL, additional — with build-time data
- Four CI/CD platforms — GitHub Actions, Azure DevOps, Jenkins, GitLab CI
- Three custom DITA-OT plugins, versioned and maintained as Git artifacts
Sample Content Assessment
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.
Submit a sample →