From document piles to verifiable datasets.
DataSnipe turns batches of PDFs, scans, and images into structured datasets with citations, confidence scores, exports, and transparent per-job costs.
The problem: trusting extraction at scale.
OCR recovered the text. OCR made documents searchable. 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.
LLMs understood the context. LLM-based 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.
From one-off prompts to repeatable workflows.
DataSnipe sits above OCR and LLMs. It uses each where it helps, and integrates the extraction steps into scalable and repeatable workflows.
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 prompt. It is to make document extraction repeatable, predictable, and ready to review, export, or run again.
Why now
Document extraction used to be constrained by OCR quality and brittle rules. Now the LLMs 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.