DeepTagger
Hi HN, I built a tool for teaching LLMs how to extract structured data from documents by annotating, not prompt engineering. I’d love your feedback. How it works: - Upload a document (DOCX, PDF, image, etc.) - Select and tag parts of it (supports nesting, arrays, custom tag structures) - Upload another document → click "predict" → see editable annotations - Amend them and save as a new example - Call the API with a third document → get JSON back Use cases: - Identify "important clauses" in contracts - Extract "total value" from invoices - Anything subjective, like "healthy ingredients" on a…
DeepTagger launched on September 17, 2025 with 386 votes, #58 of 8,160 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
DeepTagger is a no-code platform that converts documents into structured data through interactive labeling. Users highlight relevant information in sample documents, and the system uses these examples to automatically extract matching data from new documents. The platform is designed for teams who need to process large volumes of documents and includes API access for integration with other tools.
written from the facts on this page · September 2026
From the sources
DeepTagger is a no-code platform that makes your judgment scalable. It uses your annotations as an example to extract information from new documents. Highlight what matters to you once, and let DeepTagger handle the rest with precision. API access included.
We're pivoting. DeepTagger is being rethought from the ground up — same mission, sharper focus. We'll share more when we'refrom deeptagger.com
In the maker’s words, at launch
Hi HN, I built a tool for teaching LLMs how to extract structured data from documents by annotating, not prompt engineering. I’d love your feedback. How it works: - Upload a document (DOCX, PDF, image, etc.) - Select and tag parts of it (supports nesting, arrays, custom tag structures) - Upload another document →…
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