News & Updates

A technical examination of why layout segmentation and bounding boxes are essential for preserving structure, context, and traceability in production document AI systems.

Generate secure, shareable links to extraction results so anyone can view outputs instantly with no Pulse account required, while keeping enterprise controls like user defined expiration windows and access restrictions fully in your hands.

Webhooks in Pulse enable real time, event driven extraction workflows by pushing signed job status updates directly to your systems, eliminating polling overhead and reducing end to end latency.

Accuracy Scorer adds a built in evaluation layer to Pulse that automatically measures field level accuracy, detects regressions across schema and prompt changes, and gives teams continuous confidence in extraction quality as systems evolve.

A centralized system of record for extraction workflows that gives teams full version history, traceability, and safe iteration on schemas and prompts without breaking production.

The Pulse Python and TypeScript SDKs reduce integration boilerplate with fully typed, sync and async clients that provide complete parity with the Pulse API for cleaner, faster production workflows.

A rebuilt, schema-first structured output system that delivers higher accuracy on complex documents with built-in citations, programmatic corrections, and full frontend and API support.

Pulse Meridian is now available to everyone, converting complex PDFs into properly formatted spreadsheets in seconds with layout fidelity, configurable formatting, and cell-level traceability back to the source.

Pulse has processed over one billion pages in production, marking the next chapter in building reliable document intelligence for real-world workflows.

A systems-level look at how regulated enterprises evaluate document AI beyond accuracy, with emphasis on consistency, structure, and trust in production.

A specialized vision model that corrects misaligned, skewed, and rotated documents so extraction remains accurate in complex real-world workflows.

A deep dive into why charts carry the highest information density in documents and why modern AI systems consistently fail to extract their underlying data.

Pulse partners with Syntra to expand document intelligence across enterprise workflows.

Pulse introduces new models that parse complex spreadsheet structures with superior accuracy.

DeepSeek-OCR advances open document AI but lacks the determinism required for production.

Ultra Nano delivers state-of-the-art document extraction entirely within enterprise infrastructure.

Document AI pilots fail at scale where regulated industries demand deterministic, auditable outputs.

Tables fail when machines lose the structural geometry that makes data meaningful.

How enterprises scale document AI from proof-of-concept to production-grade deployments.

Financial document AI demands consistent, reproducible outputs where small errors cascade through models.

Pulse's formula recognition converts mathematical content directly into machine-readable LaTeX.

Pulse opens its platform after processing 600 million pages for Fortune 100 enterprises.

Document extraction fails across reading order, structure, and semantic axes that single metrics hide.

Pulse evaluated ByteDance's Dolphin on enterprise document tasks using standardized benchmarks.

Turn any financial document into Excel-ready data in seconds.

Traditional document processing fails exactly where financial services needs it most.

The biggest challenge in document AI is understanding how information flows across page breaks.

Pulse Ultra delivers 80% faster processing and 20% better RAG accuracy after 400 million pages.

Pulse achieves 92% accuracy on handwritten medical notes where traditional systems capture 54%.

Legacy OCR fails on nested legal tables, jurisdictional stamps, and handwritten judicial notes.

Mistral's OCR shows promise but fails on nested tables, checkboxes, and enterprise documents.

Testing Andrew Ng's document extraction service reveals over 50% hallucinated values in financial tables.

Financial institutions abandon legacy OCR due to failures on nested tables and numerical precision.

Pulse raises $3.9M led by Nat Friedman and Daniel Gross to transform enterprise document processing.

Pulse combines specialized computer vision with intelligent reading order algorithms for reliable extraction.

LLMs excel at text generation but fail at precise document extraction due to nondeterminism.