Parse more than text
Operational document parsing has to understand structure, not just characters. Cogneris identifies sections, tables, paragraphs, key-value pairs, page boundaries, and document type so downstream systems can use documents as clean data. Use it when a simple PDF parser drops table context, field labels, or citations your workflow needs.
From PDF layout to typed JSON
Native PDFs, scans, photos, and email attachments flow through OCR, layout detection, classification, extraction, and validation. The result is JSON with the context your workflow needs: page evidence, citations, confidence scores, and normalized field values.
Agent-ready document data
AI agents need dependable tool inputs. Cogneris turns document content into structured objects with evidence and validation state, so an agent can reason over invoices, contracts, onboarding packets, and claims without guessing from loose text chunks.
Layout context
Sections, tables, field labels, pages, and reading order are preserved for downstream logic.
Typed outputs
Return JSON objects, arrays, numbers, dates, currencies, booleans, and validation status.
Evidence links
Keep source page references and citations so users can verify parsed data quickly.
Tables, paragraphs, key-value fields, and citations
Cogneris parses document structure and extracts named values in the same workflow. That means a bank statement can return both the transaction table and account metadata, while a contract can return paragraphs, clause spans, and extracted obligations.
Layout-aware parsing for RAG
Retrieval systems work better when the parser preserves the document's structure. Cogneris can keep sections, tables, page numbers, field labels, reading order, embeddings metadata, and citations together so a RAG pipeline retrieves meaningful evidence instead of isolated text fragments.
Use layout-aware parsing when documents contain tables, multi-column pages, scanned forms, signatures, footnotes, clauses, attachments, or long packets where page context changes the answer.
RAG-ready JSON and Markdown context
Search and agent workflows often need both typed JSON and readable context. Cogneris can return normalized fields, table rows, source citations, and layout-aware text blocks that can be rendered as Markdown for retrieval, review, or downstream prompt context.
That keeps chunking tied to the original document structure: headings stay with their paragraphs, tables stay with captions, and extracted values stay linked to their source page and bounding-box evidence.
Chunking is not enough for operational workflows
Chunking is useful for retrieval, but back-office automation usually needs typed values, validation, routing, and audit evidence. Cogneris is built for the moment after ingestion: when parsed content has to trigger a workflow, update a record, or support a decision.
When to use parsing, extraction, or Q&A
| Job | Best for | Cogneris output |
|---|---|---|
| Parsing | Preparing document structure for systems or agents | Sections, tables, fields, citations, JSON |
| Extraction | Pulling named business fields from a document | Typed schema with values and confidence |
| Q&A | Asking grounded questions about a document | Answer, citations, reasoning trace |
For implementation detail, read the extraction API docs, classification docs, custom agents docs, or the architecture guide on ReAct document workflows.