Chicago automatically separates, identifies, and organizes documents arriving in mixed batches so downstream systems start with the right document in the right context.
Chicago addresses the digital mailroom problem by separating batches of inbound documents and identifying what each document is before further processing occurs.
Split a single uploaded file into discrete business documents such as invoices, claims, statements, forms, and supporting materials.
Use semantic understanding to distinguish similar-looking documents and reduce manual sorting effort.
Create cleaner inputs for parsing, adjudication, workflow automation, and generative insight.
Chicago is designed for high-volume environments where documents arrive in bundles, packets, or mixed uploads and need to be organized before business logic can be applied.
Files enter through a host application, workflow layer, or upstream intake process.
Chicago detects document boundaries inside mixed content and breaks them apart.
Each separated document is identified and labeled using semantic classification.
Results are passed back to the host or routed into downstream Haystac services.
Chicago is built for noisy, inconsistent, real-world inbound content.
Chicago is especially valuable anywhere inbound documents arrive mixed together.
Chicago is the correlation layer within the broader Haystac platform.
Chicago helps teams organize inbound content before extraction, decisioning, and automation begin.
