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.
Comparing approach types
RAG-style
| Feature | Parsewise | Other Approaches |
|---|---|---|
| Cross-Document Attention | ✔︎ Exhaustive cross-document attention | ➖ Top-K retrieval |
| RL from User Interactions | ✔︎ Feedback directly improves extractions | ➖ Prompt tuning, like/dislike |
| Enterprise Scalability | ✔︎ 100s of thousands of pages per run | ➖ ~10 files per run |
| KPI-Specific Models | ✔︎ Agents tuned to business KPIs | ➖ Model routing |
| Automated ontology generation | ✔︎ Auto-generated & easy to edit | ❌ No native, persistent ontology feature |
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
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
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
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
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