AI-Ready Content

A content-engineering problem, not a model problem. Structured content is the difference between RAG that retrieves accurately and RAG that hallucinates. Most AI initiatives in regulated industries fail at the content layer, not the model layer.

What gets delivered.

Content readiness assessment: Score the existing content estate on retrieval-readiness dimensions. What's recoverable, what needs remediation, what won't survive ingestion.
Chunking strategy: Section-aware chunk boundaries that preserve semantic context. Most RAG retrieval failures trace to chunking that severs claim from evidence.
Metadata schema design: Filtered-retrieval metadata, provenance tracking, version metadata, compliance attribution. Survives the pipeline.
Retrieval architecture: Vector store choices, hybrid retrieval patterns, evaluation harnesses. Built against the regulated-industry use cases that matter.
Pipeline integration: RAG ingestion pipelines, content-update propagation, deployment automation. JSONL or similar AI-ingestion-ready output formats.
Retrieval evaluation: Test sets, precision metrics, ongoing tuning processes. The evaluation harness that catches retrieval regressions before they reach production.

Outcomes.

85%: Retrieval precision in deployed RAG pipelines The precision floor we deliver — measured against representative regulated-industry test sets.

60%

Reduction in escalations when AI-assisted help is grounded in retrieved content

72%

Translation cost savings when the AI-Ready corpus is also localized

Precision measured against representative test sets per engagement before deployment closes. Adjacent outcomes are downstream effects when the AI-Ready corpus also serves chatbot, search, and translation pipelines — same engineering, different surface.

Precision matters more than recall in regulated industries. A hallucinated regulation cited as authoritative is worse than a missed one — the missed one gets escalated, the hallucinated one gets followed. 85% retrieval precision is the floor we deliver across deployed pipelines; for regulated workloads, that's not a starting point, it's the operational threshold for production.

Recent engagements.

A regulated financial-services compliance assistant.

RAG pipeline grounded in audit-tagged DITA content with metadata-filtered retrieval. Precision measured at 87% at deployment; retrieval evaluation harness deployed alongside so retrieval regressions get caught at the content-change layer. Every cited paragraph resolves to a section ID in the source repository.
An automotive field-service AI assistant.

Section-ID-stable chunks with audience, difficulty, and product-line metadata. Knowledge graph enriched with controlled-vocabulary subject scheme so retrieval can answer questions across product lines without cross-contamination. Field technicians cite back to the source procedure rather than to a paraphrase.

Anonymized for client confidentiality. Specific scope, contract details, and named outcomes available under appropriate NDA channels.

Standards and tooling.

Chunking standards: Section-aware chunking aligned to DITA topic boundaries; semantic-preserving chunk overlap patterns; metadata-first chunking for filtered retrieval.
Retrieval evaluation: Custom test sets per use case; precision and recall measurement; regression harnesses that catch retrieval drift after content changes.
Vector stores: Pinecone, Weaviate, pgvector, OpenSearch — chosen for the use case, not as a default.
Orchestration frameworks: LangChain, LlamaIndex, custom pipelines. The framework matters less than the chunking and evaluation discipline.
Output formats: JSONL for AI ingestion, structured metadata, provenance tracking. The format the LLM provider's pipeline expects.

When this goes wrong.

WHEN AI-READY ISN'T

Six-figure model investments fail at the content layer.

RAG retrieving the wrong regulation and surfacing it as authoritative. AI assistants confidently citing outdated procedures. Compliance assistants that pass smoke tests but hallucinate at edge cases. The pattern: the model was selected before the content was engineered, and the team thinks they have an AI problem when they actually have a content problem.

When you’d engage us here.

Your RAG pilot worked on the demo questions and failed on the real ones.

The corpus wasn't engineered for the actual retrieval surface. The demo set rewarded surface-level retrieval; production traffic exposes the chunking and metadata gaps.
The model team is asking you for 'better content' but can't specify what.

The handoff requires engineering decisions about chunking, metadata schemas, and retrieval filters — not just better authoring. Until those decisions are made, more content makes the problem worse, not better.
Your AI assistant cites paragraphs that aren't in the document anymore.

The provenance metadata isn't propagating through the pipeline. Section IDs aren't stable, or the index isn't being rebuilt on content updates — either way the citations are unverifiable.
You're considering fine-tuning to fix hallucinations.

Usually it's a content-engineering problem first. Fine-tuning teaches the model your voice; it doesn't fix retrieval that surfaces the wrong paragraph. The cheaper fix is upstream.

Read the AI-Ready Content narrative

Sample Content Assessment

Submit a 20-page sample. We'll return an AI-readiness diagnostic — chunking fitness, metadata gaps, retrieval-architecture implications. Two business days, no obligation to proceed. Especially valuable if you have an active AI program that's underperforming.

Submit a sample →