Parsewise achieves SOTA on Databricks OfficeQA benchmark – results

The Enterprise Layer
for Unstructured Documents

The way business users work with unstructured documents has not changed for decades.

Parsewise develops the core technology that enables exhaustive, self-learning document processing
over long time horizons, designed for real enterprise workloads.

>25k
Pages per run
>5h
Autonomous runs
>20k
Requests per minute (RPM)

Parsewise Data Engine (PDE)

PDE is built around a structured world model: a persistent, structured
representation of everything known about the task and information available.

The result is document intelligence that does not go off the rails
when scale or complexity increases.

Key Developments

Cross-Document Attention

Modeling relationships across an entire document corpus simultaneously.

  • Capture links, contradictions, and dependencies across entire corpora
  • Eliminate hallucinations by grounding outputs in all relevant sources
  • Never miss edge cases hidden outside retrieved snippets
Parsewise document list with per-document summaries

RL from User Interactions

Continuous learning system that adapts to real context.

  • Trains policies directly from real user behavior, not synthetic proxies
  • Captures domain-specific preferences that static models miss
  • Continuously improves relevance, judgment, and workflow fit
Underlying sources review with extraction highlights

Enterprise Scalability

Production-grade infrastructure for very large document packages.

  • Processes hundreds of thousands of pages per run with predictable SLAs
  • Elastic orchestration, queuing, and retries for spiky workloads
  • Central monitoring, audit, and versioning across projects
Upload files in any format: PDF, Word, Excel, PPT, images

KPI-Specific Models

Precision models tuned and validated for business KPIs.

  • Built for narrow, high-value tasks using targeted fine-tuning
  • Outperform general models on structure, accuracy, and edge-case handling
  • Capture domain logic from real documents and user habits
Agent configuration: name, extraction task, cell type and unit

Automated Ontology Generation

Business-ready structure without engineers.

  • Generates and updates domain ontologies through natural interaction
  • Removes technical barriers, enabling teams to adapt structure
  • Integrates cleanly with existing databases and enterprise systems
Describe what you would like to extract

Join us!

If you have world-class experience in any of the above areas or adjacent fields, please reach out. We are always hiring exception engineers!