Why generic chunking breaks on business documents
Invoices, statements, contracts, policies, claims, and onboarding packets carry meaning in their layout. A table row, signature, clause, checkbox, or page footer can change the answer. Cogneris parses document structure before chunking so retrieval does not separate values from their evidence.
Layout-aware chunks
Keep headings, paragraphs, tables, captions, and page context together.
Citation-first output
Return source page references and bounding boxes alongside extracted values.
Validated fields
Use typed JSON when a workflow needs reliable values, not just search snippets.
RAG preprocessing API for documents
A RAG preprocessing API should do more than split text every few hundred tokens. It should preserve reading order, headings, tables, page references, document type, image-derived text, and source citations so retrieval can explain where an answer came from.
Cogneris can prepare both retrieval-friendly context and workflow-ready data from the same document: Markdown for prompts, chunks for vector search, JSON for automation, and citations for review.
Outputs for RAG and agents
| Need | Output | Why it matters |
|---|---|---|
| Retrieval | Layout-aware text blocks and Markdown | Preserves structure for better prompts and source previews |
| Automation | Typed JSON with validation state | Lets software route, approve, or reject records |
| Audit | Citations, page references, and bounding boxes | Lets reviewers trace answers to source documents |
When to use document AI for RAG
Use this pattern when your documents contain tables, financial values, dense clauses, multi-page packets, scans, IDs, handwritten fields, or forms where visual proximity matters. The same parsed output can feed vector search, an agent workflow, a review UI, and a downstream system of record.
RAG parsing vs extraction APIs
Unstructured and LlamaParse-style tools are strong when the goal is to prepare documents for retrieval. Extraction APIs are stronger when the answer needs to become a record, approval, exception, or workflow event. Many production systems need both: parsed chunks for search and typed JSON for action.
| Decision | Use RAG parsing when | Use extraction APIs when |
|---|---|---|
| Output | Markdown, chunks, layout blocks, images, and table context | Typed JSON fields, arrays, validation state, and audit metadata |
| Primary user | AI search, support copilots, research agents, knowledge bases | AP, underwriting, claims, onboarding, compliance, and product workflows |
| Success metric | Answer relevance and citation quality | Field accuracy, STP rate, false approvals, and review effort |
| Failure mode | Wrong or ungrounded answer | Wrong value posted to a system of record |
Recommended architecture
Parse once, then branch. Send layout-aware chunks and source citations to retrieval. Send validated fields, tables, and business rules to workflow automation. Keep both outputs tied to the same document ID so an agent can answer questions and the application can still trace every decision back to the original page.
Example parsed output
The same parse can expose Markdown, chunk metadata, table rows, and extracted fields. That lets a retrieval system show the source paragraph while a workflow system stores the normalized value.
| Object | Example fields | Use |
|---|---|---|
| chunk | text, markdown, page, section, citation, bbox | Vector search and grounded answers |
| table | caption, columns, rows, page, confidence | Financial tables, line items, schedules, transactions |
| field | name, value, type, confidence, citation, validation | Workflow automation and system-of-record updates |