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What is Penquify?

From Chilean slang “penca” (lousy, worse) — because your document photos should look realistically bad, not studio-perfect.
Penquify is an open-source Python toolkit that takes structured data and produces photorealistic smartphone photos of printed logistics documents — with coffee stains, folds, blur, skew, and every imperfection that makes real-world document processing hard. You don’t build the PDF. You give penquify an OC number, a JSON payload, or upload an existing PDF — and it generates the document, introduces realistic discrepancies, renders it, photographs it, verifies every field, and tells you exactly what’s occluded. Penquify output: realistic warehouse photo of dispatch guide

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The Problem

You’re building a vision pipeline — document extraction, agentic workflows, OCR automation. But you have 12 real documents. You need 1,200 test cases covering blurry, folded, stained, cropped scenarios. And the data in each photo has to be correct and verifiable because your agent needs to do downstream lookups. Scanning the same invoice 50 times doesn’t help. Image augmentation (rotate, noise) doesn’t produce realistic warehouse photos. And manually photographing documents with different phones, angles, and lighting doesn’t scale.

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The Solution

ERP purchase order       penquify generates        penquify generates
(or any JSON/PDF)  ──►   dispatch guide PDF    ──► realistic photos
                         with supplier jargon,     with verified
                         unit mismatches,          ground truth +
                         realistic discrepancies   occlusion manifest

Structured Data In

JSON payload, uploaded PDF, or natural language description. Penquify generates the document with realistic supplier names, unit mismatches, and quantity discrepancies.

Realistic Photos Out

Photorealistic smartphone photos — configurable camera model, paper deformation, stains, blur, angle, glare. 8 presets + infinite custom.

Ground Truth Verified

Blind extraction + programmatic comparison. The model never sees the answers. Every field verified. Mismatches trigger retries. Occlusion manifest explains what’s hidden and why.

Every Interface

CLI tool, Python library, REST API, MCP server (5 tools for Claude/Cursor), Agent SDK plugin, Docker/K8s deployment.

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Before → After

Input: Clean PDF (auto-generated)Clean document

Output: Warehouse Photo (verified)Realistic photo

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Same Document, Different Nightmares

Every photo below was generated from the same clean PDF. Each preset targets a different real-world failure mode.

full_picturefull_picture — clean handheld

folded_skewedfolded_skewed — dog-ear, crease, tilt

coffee_staincoffee_stain — stain over text

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How It Works

1

Define or upload a document

Provide structured data (JSON), upload a PDF/image, or just run penquify demo. Penquify generates a realistic document with supplier-style names (not your ERP master data names), unit mismatches, and configurable discrepancies.
2

Render clean PDF

Jinja2 HTML templates produce a pixel-perfect PDF. Dispatch guides, invoices, POs, BOLs — or bring your own template.
3

Generate photo variations

Each variation is sent to Gemini image generation with a fixed system instruction enforcing photorealistic operational capture. Camera model, paper deformation, stains — all configurable.
4

Verify ground truth

A separate vision model blindly extracts fields from the generated photo (it never sees the expected values). Python compares extracted vs source. Mismatches trigger retries with correction prompts.
5

Build occlusion manifest

If a variation intentionally hides data (crop, stain, fold), the manifest reports exactly which fields are affected: "oc_number": "occluded_by_crop", "item_3_qty": "obscured_by_stain".

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Quick Start

pip install penquify
playwright install chromium
export GEMINI_API_KEY=your-key

# Full demo: document + 8 verified photo variations
penquify demo

# Upload any existing PDF
penquify upload --image invoice.pdf

# Describe what you want
penquify config --text "folded paper with grease, shot on old Motorola"

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Real Mismatches

The kind of discrepancies penquify generates — the same ones real supplier documents have:
Dispatch Guide (supplier)Purchase Order (ERP)Challenge
PAPA PREFRITA CONGELADA 12 CJPAPAS FRITAS 10MM 150 KGDifferent name + unit. No weight per case.
MOZZARELLA RALLADA 115 KGQUESO MOZZARELLA RALLADO 120 KGDifferent name + 5kg short.
JENGIBRE FRESCO PELADO 2 UNJENGIBRE 0.5 KGUN vs KG. No weight per unit.
LIMON SUTIL FRESCO 24 KGLIMON SUTIL 25 LKG vs L + 1 unit short.
MENTA FRESCA ATADO 10 UNMENTA FRESCA 2 KGAtados vs KG. No weight per atado.

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Open Source

MIT licensed. Self-host free forever. GitHub →