Kalque translates scientific papers, technical books and course packs into your language — with every figure in place, every formula untouched, every reference intact. Then it hands you the report that proves it.
The problem
These aren't our words — they're the incumbents' documentation and real users, verbatim. Every quote links to its source.
"You can find text in images and scanned .pdf pages in the output document but they aren't translated."
Google Translate official help — support.google.com"Data tables, multi-column formats, and labeled graphs — may experience some formatting loss in PDFs."
Google Cloud Translation docs — docs.cloud.google.com"It destroys the formatting completely. It changes the font, the font size and often the color." — confirmed by Microsoft: "yes the fonts, font size, and font color changed."
Microsoft Q&A — learn.microsoft.com"There's a lot of services which can do this, but those break the formatting. I've even tried to make a custom python app to do this, but the formatting breaks always."
r/machinetranslation, Feb 2025 — reddit.com"Professional translators charge €1,000 to €3,000 per book, and take 2–3 weeks to deliver."
r/selfpublish (author of 250+ books) — reddit.com"Parsing problems in author/reference sections (resulting in merged paragraphs)."
Self-admitted limitation, README of the leading open-source engine — github.com/funstory-ai/BabelDOCThe cost of this mess is peer-reviewed: non-native English speakers spend 46.6–90.8% more time reading and ~30–50% more time writing papers in English — about 19 extra working days a year (Amano et al. 2023, PLOS Biology, n=908).
How it works
Kalque never feeds your document to a translator as a blob of text. It builds a structural model first, locks everything that must not change, and verifies the result segment by segment.
Your PDF/EPUB/DOCX becomes a document model: sections, paragraphs, figures, equations, references, cross-links.
Citations, formulas, numbers, links and figure anchors are sealed as untouchable tokens. References are never translated — by rule.
An LLM engine translates segment by segment with your field's terminology — context-aware, register-aware, glossary-locked.
Every segment is machine-checked: tokens, numbers, tags, coverage. You get the document — and the Fidelity Report.
Proof, not promises
This is a real report from a real job: the 2023 PLOS Biology paper on language barriers in science — 26 published pages, 6 figures, 51 references, 6,352 words — translated EN → PT-BR by the demo pipeline in this repo.
If a check ever fails, you see exactly which segment and why, before you pay. That's the deal: we show our work, or you don't trust us.
Amano et al. (2023) · PLOS Biology · EN → PT-BR · 79 segments
| Coverage — every segment translated | 79/79 · 100% ✓ |
| Citation & link tokens intact | 79/79 · 100% ✓ |
| Numbers & statistics preserved | 79/79 · 100% ✓ |
| Formatting tags balanced | 79/79 · 100% ✓ |
| References translated (by rule: never) | 0/51 · 0% ✓ |
Pricing
Human translation of a technical book runs €1,000–3,000 (and $0.06–0.11/word at academic services). Free tools cap at 300 pages and break your figures. Kalque is one plan: unlimited pages, one price.
Honest FAQ
Per our own voice rules: admit limits before you find them.
Born-digital scientific PDFs and structured formats (JATS XML, EPUB, DOCX, LaTeX-derived PDFs): body text, headings, figure captions, tables, inline/display math (protected, never re-typeset), citations and reference lists (protected, never translated). The demo in this repo is a full 26-page paper with 6 figures and 51 references, all checks green.
Scanned/photographed documents (OCR introduces its own errors — same caveat DeepL makes), handwritten notes, text drawn inside figure images (we preserve the figure; we don't yet repaint the labels), poetry and wordplay, and right-to-left scripts (roadmap). If your document is one of these, the free preview will show you exactly what you'd get before any payment.
No. The translation layer is an LLM engine chosen for context-awareness (the same class of model users report as "100% accurate and native" in the research linked above), but the product is the structure model around it: parse → protect → translate → verify. The verify step alone — the Fidelity Report — doesn't exist in any tool we benchmarked (14 competitors, table in the repo).
For your own personal reading, translating a lawfully obtained copy is broadly analogous to format-shifting, but laws differ by country and redistribution is never OK. Kalque stamps outputs with source attribution and license notes (the demo carries its CC-BY line), and personal-use terms are enforced at checkout. Publishers get a separate licensed workflow.
Processing is job-scoped; files are deleted after delivery unless you opt into a library. (Commitment for launch; infrastructure details will be published when the hosted service ships.)