Trellis

Unlike traditional OCR and template-based extraction, Trellis uses large language models to contextually understand document content. This allows the platform to accurately extract data even when layouts vary between document versions, vendors, or formats — reducing the need for constant template maintenance and handling real-world document messiness out of the box.

Users can define structured output schemas for each document type, specifying exactly which fields to extract and how they should be formatted. This ensures that extracted data maps directly to downstream system requirements — whether populating a database, feeding an ERP, or triggering a workflow — without additional transformation steps.

Trellis processes PDFs, scanned images, spreadsheets, and Word documents through a single unified pipeline. Teams do not need separate tools for different file types, which simplifies architecture and reduces the number of integrations required to handle diverse document inputs from multiple sources.

The platform exposes a developer-friendly REST API for document ingestion, status tracking, and structured data retrieval. This enables engineering teams to embed Trellis directly into automated pipelines, CI/CD workflows, or application backends without manual intervention or UI-dependent processes.

Trellis supports high-volume batch processing, enabling organizations to submit large queues of documents for extraction in a single operation. This is critical for industries like insurance, finance, and healthcare where thousands of documents arrive daily and must be processed with consistent accuracy and minimal latency.