Hyperscience

Hyperscience's proprietary Vision Language Model introduced in the Spring 2026 release processes documents by understanding visual layout, context, and field relationships rather than just performing character-level OCR. ORCA powers the platform's ability to handle semi-structured and unstructured documents without rigid template configuration, recognizing data fields based on spatial relationships and semantic context. This approach enables the platform to adapt to document format variations — such as different insurance claim form layouts from multiple carriers — without requiring separate templates for each variant.

The Hypercell platform provides purpose-built solutions for specific industry workflows rather than requiring ground-up configuration. Current vertical products include Hypercell for Freight Pay (automating delivery-to-cash document workflows for logistics companies), Hypercell for SNAP (processing government food assistance benefit applications, named Solution of the Year at the 2025 GovAI Summit), and Hypercell for GenAI (applying generative AI capabilities to document understanding). Each Hypercell comes with pre-trained models, pre-configured workflows, and industry-specific validation rules that significantly reduce time-to-value compared to building custom document processing pipelines from scratch.

Hyperscience's hybrid automation architecture routes documents through configurable confidence thresholds — extractions above the threshold are straight-through processed while those below are queued for human review. Every human correction is fed back into the ML models as training data, creating a continuous learning loop that improves accuracy over time on each customer's specific document corpus. Organizations can adjust confidence thresholds to balance automation rates against accuracy requirements, and the system provides detailed analytics on extraction confidence, human correction rates, and model improvement trends.

Unlike cloud-only competitors, Hyperscience supports deployment across public cloud, private cloud, on-premises data centers, and fully air-gapped environments with no internet connectivity. This capability, combined with FedRAMP Authorization and SOC 2 Type II certification, makes it one of the only IDP platforms viable for U.S. federal government, defense, and intelligence community use cases where data sovereignty and network isolation are non-negotiable requirements. All deployment models maintain full feature parity, ensuring organizations do not sacrifice capabilities for security.

The platform's ML-powered OCR engine supports over 140 languages for printed text and provides industry-leading handwriting recognition including cursive, mixed print-cursive, and degraded handwriting common in medical and legal documents. Backed by models trained on billions of data points, the handwriting recognition capability is a key differentiator for use cases like processing handwritten clinical notes, insurance adjuster reports, and legacy government forms where other IDP platforms struggle with accuracy.