Tenderness renders synthetic labeled documents via Cairo/Pango to build training datasets for VLM and OCR models.
Yes, Tenderness is open source under the Apache-2.0 license.
Tenderness is free to use.
Yes, Tenderness can be self-hosted (the source is available under the Apache-2.0 license).
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.
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.