The decisions you make about how content is structured determine what every downstream system can do with it. Schema, taxonomy, metadata, reuse — the foundation that pays back across every engagement.
45%
Content reuse rate enabled by reuse-aware architecture
72%
Translation cost savings downstream of conditional content design
Anchor metric measured at 6-12 months post-rollout across regulated-industry engagements. Adjacent outcomes are downstream effects of the architecture work — measured during the first localized release after migration.
The connection from IA quality to support volume is direct. Bad IA produces content users can't find, can't follow, or can't trust — every one of which surfaces as a support ticket. Architecture quality is measurable in escalation volume; if support tickets are climbing, the IA is the upstream cause more often than the writing.
A Class III medical-device IFU consolidation.
Redesigned the content model across multiple device families with regulatory profiling for FDA, EU MDR, and equivalent international standards. Reduced authored topics by more than 60% while the number of markets served expanded. The reuse architecture made the subsequent localization wave possible without doubling translation headcount.
A multi-agency federal compliance architecture.
S1000D specialization plus a subject scheme aligned to two different mission taxonomies. Subject-scheme keys carried the audience and classification metadata that downstream retrieval and access-control systems needed. Reuse across agencies' deliverables without parallel authoring streams.
Anonymized for client confidentiality. Specific scope, contract details, and named outcomes available under appropriate NDA channels.
WHEN IA IS WRONG
Failed migrations because there's no clean target to migrate to. Reuse that doesn't work because the granularity is wrong. RAG retrieving the wrong chunks because semantic boundaries weren't designed in. Translation that costs more every quarter because there's no separation of concerns. The architecture decisions get re-litigated in every phase that follows.
Two CCMS implementations have already failed.
Usually means the content model was never finalized — the platform was picked first, and authors then fought the configuration. The fix is in the model, not the next platform.
Translation costs are climbing faster than content volume.
The reuse architecture isn't doing its job. New content is being authored as variants of existing content rather than as references to it; translation is paying for the redundancy.
Your RAG pilot retrieves the wrong paragraph 35% of the time.
The chunk boundaries don't match the semantic boundaries. Architecture design (section-ID stability, conref structure, subject scheme) determines what retrieval can recover; the model can't make up for it downstream.
Auditors keep finding the same gap in different documents.
The metadata schema isn't enforcing what it needs to. Compliance attributes are present in some topics and missing in others — usually because the architecture didn't gate them at validation time.
Submit a 20-page sample. We'll return a content-model fitness assessment — what the existing IA can support, what it can't, and what redesign would make possible. Two business days, no obligation to proceed.
Submit a sample →