Conventional Chunk
- very long
- spanning multiple sections
- isolating heading from subsequent content
PrimeCut is POMA AI's patent-protected document ingestion & RAG chunking core — structure-aware across text, tables, scanned PDFs (OCR), and 50+ filetypes.
The structural awareness matters most on technical documents — research papers, financial filings, engineering specs — where naive chunkers split a single argument across three chunks and break retrieval. Drop it into your existing pipeline; retrieval, embeddings, and vector store stay where they are.
Every document carries an internal logic: a hierarchy of headings, sub-sections, tables, lists, and supporting elements that define what content belongs together and why. That structure is not decoration — it is the semantic map of the document.
Standard ingestion pipelines discard this map. They extract raw text and hand it to a chunker that has no knowledge of where one idea ends and another begins.
PrimeCut understands your document’s content hierarchy before chunking — preserving structural relationships, eliminating context poisoning, and producing semantically coherent chunksets that make every downstream RAG component more accurate by default.
Send us a document — any supported filetype — and you get back a POMA archive: a zip containing the structured SDK output your pipeline can read directly. No glue code, no per-file branching.
The archive bundles the chunks your retrieval layer consumes alongside the intermediate artifacts that produced them, so you can debug, re-process, or surface source content without re-running the pipeline.
chunks.jsonchunksets.jsonimage_sources.jsonassets/Every archive also carries the intermediate artifacts that produced those chunks: input as markdown and HTML, structurally-indented plain text, AI/OCR image descriptions, extracted tables, pre-processed source files, and archive- and content-level metadata.
PrimeCut treats each document as a structure, not as just bytes. It detects the hierarchy of your document - headings, subsections, and clauses. This allows it to preserves clauses with their definitions, tables with their captions, and graph content in their section. It then emits chunksets that your embedding model can use directly.
Headings, tables, lists, and captions retain their hierarchical relationships through chunking. No flattening to character runs.
PDF, DOCX, PPTX, XLSX, HTML — same engine for all of them. Images, charts, and tabulated data are handled inline as searchable content.
Output is structured JSON: chunks with full ancestor metadata and ready-to-embed traversal paths. Drop into any embedding model or vector DB.
Cybersecurity Guidance for Medical Devices: Quality Systems and Premarket Submission
Requirements
[…]
Guidance for Industry and Food and Drug Administration Staff
[…]
Contains Nonbinding Recommendations outline
[…]
B. Designing for Security
When reviewing premarket submissions, FDA intends to assess device cybersecurity
based on a number of factors, including, but not limited to, the device's ability
to provide and implement the security objectives below throughout the device
architecture.
[…]
The extent to which security requirements, architecture, supply chain, and
implementation are needed to meet these objectives will depend on but may not
be limited to:
[…]
• Its intended and actual environment of use:
[…]
• The risk of patient harm due to vulnerability exploitation.Standard chunking ignores how your documents are structured. So a query like 'How high was the interest rate last year?' retrieves a wide net of chunks where most of the content has nothing to do with the question — and you still pay for every token returned. PrimeCut chunks structure-aware: queries return only the relevant content, no loss of recall.
PrimeCut ships in two tiers. Both preserve document hierarchy. Both eliminate context poisoning. The difference is in how they handle visual content and compute — matched to the complexity of your documents and your budget.
Simple hierarchical chunking for well-structured documents.
Full structural and visual intelligence for complex, mixed-content documents.
Structured files —
xml, cir, json, yaml,
toml, ini, env, csv,
tsv, xls, xlsx, xlsb —
are always chunked in Pro mode and billed at the Eco rate.
PrimeCut sits at the ingestion layer of your RAG pipeline — upstream of your vector database, your embedding model, and your retrieval logic. It receives documents. It returns structured, hierarchically-bounded chunksets.
The SDK is lightweight. The API is flexible. And because PrimeCut's output schema is consistent across both configurations.
Compatible with:The SDK is lightweight. The API is flexible. And PrimeCut's output schema is consistent across both configurations.
Free tier covers 1,000 pages — drop the SDK in, point it at a document, see what comes back. No retrieval refactor, no vector DB swap, no architectural overhaul.
1,000 free pages. No credit card required.