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Unstract: LLM-Powered Document Extraction That Plugs Into Your Pipeline
Extract structured data from messy PDFs and documents without writing custom parsing logic. Ship in days instead of weeks.
Zipstack/unstract ↗You have a pile of invoices, contracts, or forms. You need specific fields pulled out and fed into a database or API. Normally you'd hand-code a parser or hire someone to build extraction logic. Unstract flips this: feed it a document type, point an LLM at it, and it learns what to pull. The output goes straight into your ETL pipeline or API endpoint. No regex. No brittle rules. The catch: it's still LLM-dependent, so quality depends on prompt and model choice. But if you're already using AI in your stack, this saves weeks of glue code.
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Unstract: Document extraction that actually ships
LLM-driven extraction for unstructured data. Point it at PDFs, contracts, invoices. Get structured output into your API or ETL pipeline. No custom parsing code. Weeks to days. Worth testing if you're extracting at scale.
document extraction used to mean hand-coded parsers or hiring someone to build it. unstract lets you feed an LLM your document type, get structured output into your API. less glue code, faster shipping. worth the test if you're pulling data from pdfs at scale.
If your team is drowning in manual document processing or building custom extraction logic for invoices, contracts, or forms, Unstract offers a cleaner path. LLM-native extraction, API-ready output, ETL integration. Weeks of dev time condensed into hours of configuration. Built for builders shipping at speed.
Built document extraction the hard way once. Watched teams burn weeks on custom parsers for PDFs, emails, scans. Unstract flips that: LLM-native extraction that hooks straight into your API or ETL pipeline. No more regex hell. Just schema in, structured data out. 14k+ stars on the repo tells me I'm not the only one tired of reinventing this wheel.
unstructured data extraction used to be the tax you paid before real work. unstract makes it a single step. LLM-driven, API-ready, pipeline-native. schema → structured output. that's it. (14k stars because builders keep reaching for this instead of writing extraction code from scratch)