The Enterprise Layer
for Unstructured Documents

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.

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.

Users define an extraction task. Parsewise runs autonomously for hours, coordinating many models and agents until the objective is fully resolved.

>25k

Pages per run

Pages per run

>5h

Autonomous runs

Autonomous runs

>20k

>20k

Requests per minute (RPM)

Requests per minute (RPM)

Parsewise Data Engine (PDE)

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

Cross-Document Attention

✔︎

Exhaustive cross-document attention


Top-K retrieval


Top-K retrieval


Top-K retrieval

RL from User Interactions

✔︎


Feedback directly improves extractions

✔︎


Feedback directly improves extractions


Prompt tuning,
like/ dislike


Requires custom evaluation pipelines

Enterprise Scalability

✔︎


100s of thousands of pages per run


~10 files per run

✔︎


Requires custom
retrieval logic

✔︎


Requires custom
retrieval logic

KPI-Specific Models

✔︎


Agents tuned to
business KPIs


Model routing

✔︎


Requires custom implementations

Automated ontology generation

✔︎


Auto-generated &
easy to edit

✔︎


Auto-generated,
easy to edit


No native, persistent ontology feature


No native, persistent ontology feature

Key Developments

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.

Automated Ontology Generation

Business-ready structure without engineers.

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

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!

Email us

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