Publications outlive the people who understood them.

A build that won't run is a memory problem.

A data module fails validation. The log points at a missing entity, an unresolved reference, a business rule violated — somewhere across thousands of files of SGML or XML, schemas, catalogs, and project rules. The person who has seen this exact failure before left two contracts ago. So the diagnosis starts from zero: archaeology, not memory.

Continuum's answer is the same one it gives engineering teams: put the whole working corpus — sources, schemas, logs, and the history of every fix — into one persistent, searchable memory, and never start from zero again.

An architecture that already fits.

Technical publications are the rare corpus that arrives pre-structured. S1000D data modules are chunked, versioned, and reuse-oriented by design. IETM work is retrieval over structured content. Reuse detection is semantic search. And the configuration-management culture — every change traced, every issue numbered — is the same discipline Continuum's decision ledger is built on. The memory underneath Continuum is a general engine for structured knowledge; publications fit it naturally.

Your repository stays authoritative. Memory wraps around it.

Programs keep their source where it belongs — a CSDB on S1000D programs, a CCMS in DITA shops, a controlled document server or file share on legacy SGML projects. Continuum doesn't replace it and doesn't ask for credentials to it. It works from what programs already produce: a controlled export, a checked-out working package, build and validation logs, schemas and catalogs.

That package is ingested into a working memory — searchable semantically when you know what you mean but not where it lives, and by exact text when you're hunting a specific data module code, entity, or error string — alongside every diagnosis, fix, and validation that follows.

Every fix becomes permanent memory.

An engagement closes the way a Continuum session closes: the root cause recorded, the change committed, the validation results kept, all of it ingested. When the same publication breaks again — new export, new log, same family of failure — the first question is "what did we fix last time?", and the memory answers: which module, which reference, which rule, and why.

Future engagements start from memory, not archaeology.

Built for networks with no path out.

Engram — the memory — runs air-gapped: embeddings served by a local model on your hardware, over an OpenAI-compatible endpoint, its one outside dependency brought inside the boundary. Your export, your memory, and every query stay in the enclave; nothing about your corpus leaves it. That's what we've built and run today.

The full authoring workflow inside the boundary — the agents, not just the memory — is a build we'll take on with the right program, not a capability we claim ships. If that's you, that's a conversation.

What's true today, and what isn't yet.

Today, Continuum treats publication sources the way it treats any corpus: ingested as text, chunked, embedded, fully searchable — modules, schemas, catalogs, logs, and the engagement history in one store, served to your agents over the same protocol and the same OAuth 2.1 contract as everything else. Air-gapped deployment is true today too — the validated local-embedding profile, not a roadmap item. Structure-aware S1000D ingest — data-module metadata, element-level chunking, reference tracing — is on the roadmap, and it is stated here as roadmap, not as a feature. When the answer is "not yet," that's the answer you'll get.