DataSnipe turns batches of PDFs, scans, and images into structured datasets with citations, confidence scores, exports, and transparent per-job costs.
OCR made documents searchable, but search is not extraction. It can recover text from scans, yet it does not understand what the text means, map varied wording to requested fields, or show a reviewer the evidence behind each extracted value.
Chat-based AI tools made summaries and extraction possible across a wide range of formats. But the approach breaks at scale: rate limits, context limits, retries, copy-paste workflows, and outputs that drift because each conversation is a little different.
A team needs the same fields extracted the same way across every document, with visible costs and a citation for each value. Otherwise the work just moves from manual extraction to manual checking, cleanup, and reconciliation.
DataSnipe sits above OCR and LLMs. It uses each where it helps, then wraps the extraction in the operational pieces needed for batch work.
Users define the fields they need, upload a batch of documents, choose a model and budget, then review the same schema across every file.
Each result stays close to the source material: every extracted field has a citation, every run has a visible cost, and every output is ready to export into a spreadsheet or downstream review process.
The point is not to replace a good one-off ChatGPT extraction. It is to make document extraction repeatable, predictable, and ready to review, export, or run again.
Document extraction used to be constrained by OCR quality and brittle rules. Now the models are good enough to understand varied document formats, but the best model for a job can change quickly.
That makes comparison part of the workflow. Teams need to rerun the same extraction against different providers, inspect the differences, and decide which output is good enough for the work in front of them.
The pattern applies anywhere teams need structured data from inconsistent document sets: invoices, receipts, contracts, research papers, and other workflows where manual review is expensive but blind automation is not acceptable.
DataSnipe comes from a simple frustration: extraction is easy to demo, but hard to trust, repeat, and use at scale.
Lorenzo Barasti is the founder of DataSnipe. He has spent more than a decade building data-heavy products and engineering teams across logistics, healthcare AI, data warehousing, and equity management.
His background in mathematics and production data systems shapes the product: outputs should be structured enough for machines, traceable enough for review, and repeatable enough for teams.