# Tenderness

> Updated 2026-09-23 · type: tool · category: ai-infrastructure · status: active · rev 1

Tenderness renders synthetic labeled documents via Cairo/Pango to build training datasets for VLM and OCR models.

- Open source: yes (Apache-2.0)
- Self-hostable: yes
- Pricing model: free
- Best for: An ML or data engineer building a document-extraction, OCR or VLM pipeline who needs deterministic, labeled page images as training data and wants to generate them in code rather than scrape and annotate real documents.
- Last verified: 2026-09-23

- **Canonical:** https://gtmstacker.com/registry/tool/tenderness/
- **Source:** [github · paperchase-labs/tenderness](https://github.com/paperchase-labs/tenderness)
- **Tags:** ai-infrastructure, data-scraping, self-hostable, ocr, synthetic-data, python-library
- **Repository:** https://github.com/paperchase-labs/tenderness

## Is Tenderness open source?

Yes, Tenderness is open source under the Apache-2.0 license.

## How much does Tenderness cost?

Tenderness is free to use.

## Can I self-host Tenderness?

Yes, Tenderness can be self-hosted (the source is available under the Apache-2.0 license).


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Apache-2.0 Python library that renders synthetic, deterministic documents from text and images via Cairo/Pango to produce labeled datasets for training vision-language and OCR models. It is a rendering library, not a hosted generator app — you call it in code to emit page images with known ground-truth, so the data-prep layer for a document-extraction model is reproducible instead of hand-collected. Early and small (7 stars), it targets teams that need controlled training data rather than a turnkey product.

## Provenance

- Apache-2.0 (OSI-open, confirmed in repo metadata); 7 stars, last push 2026-08-04; installs via `pip install tenderness`; renders text and images to documents through Cairo/Pango to generate labeled datasets for VLM/OCR training (WebFetch 2026-09-23, github.com/paperchase-labs/tenderness).
- Framing correction applied: this is a rendering library, not a general-purpose "generator" application — it is imported and driven from your own code.
- Curated from the GTM Stacker signal registry (2026-09-23 pass); license/facts independently verified 2026-09-23.

## Why it matters for a GTM stack

Document extraction is a common enrichment primitive — parse invoices, contracts, forms, scanned lead lists — and the bottleneck is almost never the model, it is labeled data that matches your document shapes. Tenderness sits underneath that: it renders synthetic pages with known ground-truth deterministically, so a team can generate the exact training set a custom VLM/OCR extractor needs instead of scraping and hand-annotating real documents. For a RevOps or data team building its own document-enrichment pipeline, that is the reproducible data-prep layer. The honest read: it is small (7 stars, last pushed early August 2026) and deliberately narrow — a library for making training data, not a system that trains, reads or extracts anything on its own.