Case studyArchitecture, governance, and how to adapt this pattern in a pilot
Business use case
Pilots fail when documents are “in the system” but retrieval returns junk. Before you buy a vector database, you need a repeatable ingestion shape: chunking rules, stable ids, and citations you can show to risk teams.
Solution
This example chunks text deterministically (no external services) and emits citation ids + titles you can attach to downstream retrieval and UI.
Why it matters
- Auditability: stable chunk ids make answers defensible
- Refresh cost: deterministic chunking reduces accidental churn
- RAG quality ops: you can measure empty retrieval and fix ingestion, not prompts
Delivery playbookDiscovery → pilot → scale
- 1Discovery2–4 wks
Inventory document sources and ownership; agree chunking rules and citation requirements with risk.
- 2Pilot6–8 wks
Chunk one domain (HR/policy) deterministically; measure retrieval hit rate and empty-retrieval rate on golden questions.
- 3Scaleongoing
Automate refresh and change detection; add observability for ingestion drift and re-chunk triggers.
Where else this appliesIngestion is the quiet determinant of whether RAG works. The same chunking + citation approach applies anywhere you need auditable grounding from messy sources.
Policy corpora
Prepare HR/legal policies with stable ids so answers can cite and audits can trace.
Runbooks and SOPs
Chunk operational procedures so on-call assistants can quote exact steps.
Customer contracts
Split long PDFs into consistent sections for citation-required Q&A.
Product docs
Normalize docs before enabling self-serve support assistants.
A portable local chunker is a good first step before you commit to a managed search or vector store; you can still measure retrieval quality and drift.