Skwiz

Users describe the data they want in plain language, then run extraction across PDFs and images. The approach avoids long training cycles and reduces setup from weeks to minutes, so teams move quickly from pilot to production without labeling hundreds of examples.

A library of templates helps teams start with common document types, then customize fields and logic in multiple languages. This supports shared configurations across regions and reduces duplicate work when rolling out similar extraction projects globally.

Incoming files are automatically classified, and large PDFs can be split into individual documents with a single toggle. Linked classification after splitting keeps processing aligned to the right template, which reduces manual sorting and speeds batch intake.

Extraction supports tabular data and presents results for quick review. Analysts can verify totals and line items, resolve edge cases, and export clean outputs for downstream systems, which cuts reconciliation time in finance and claims workflows.

An API connects Skwiz to existing applications, while recent updates add fine-tuning of generative AI models for specific document types. This combination lets organizations automate ingestion and continuously improve Skwiz features on their own data.