How PeterParser Achieves 99.5% Table Accuracy

Most document parsing tools try to do everything in a single pass: read the PDF, understand the content, and output structured data. The result is mediocre accuracy across the board. PeterParser takes a different approach — a three-stage pipeline where each stage is purpose-built for one job.

Stage 1: Document Conversion

The first stage converts raw documents (PDFs, images, DOCX) into clean, structured markdown. This isn't naive text extraction — it preserves table structure, reading order across multi-column layouts, and handles OCR for scanned documents. The output is markdown that downstream AI can actually reason about, not garbled text with broken tables.

Stage 2: Structured Extraction

The structured markdown goes to our AI extraction engine with a JSON schema — either one of 16 built-in presets or your custom output template. The engine extracts exactly the fields you need with deterministic output. The same document produces the same JSON every time. No prompt engineering required on your side.

Stage 3: Source Grounding

Every extracted value gets traced back to its exact position in the source text. Click on “$2,160.00” in the output and see it came from page 1, characters 245-254, in the context of “Total Due: $2,160.00”. This is the audit trail that regulated industries require.

Why Three Stages Instead of One

• → Stage 1 handles layout. AI models are terrible at understanding PDF coordinates and page geometry. A dedicated conversion stage solves this.
• → Stage 2 handles semantics. It understands that “Total” means different things on an invoice vs a receipt.
• → Stage 3 handles trust. Extraction without provenance is guessing. Grounding turns guesses into verifiable facts.

Each stage is independently upgradeable. When better technology becomes available, we swap it in without changing the API contract. Your integration code never changes.

The Fast Lane

Not every document needs the full pipeline. Set pre_processing: false to use our lightweight text extraction — 10x faster, lower cost, no surcharge. Ideal for text-heavy documents where table detection and structured extraction aren't needed.