Research to transform how businesses work with unstructured documents.

Research to transform how businesses work with unstructured documents.

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

Parsewise Labs develops the core technology that enables
mapping large document corpora to enterprise ontologies.

Parsewise Data Engine (PDE)

Parsewise Data Engine (PDE)

Our proprietary framework for next-generation information extraction and resolution.

Comparing
approach types

RAG-style

Cross-Document Attention

Cross-Document Attention

✔︎


Exhaustive cross-document attention

✔︎


Exhaustive cross-document attention


Top-K retrieval


Top-K retrieval


Top-K retrieval


Top-K retrieval

RL from User Interactions

RL from User Interactions

✔︎


Feedback directly improves extractions

✔︎


Feedback directly improves extractions


Prompt tuning,
like/ dislike


Prompt tuning,
like/ dislike

✔︎


In custom multi-stage pipelines

✔︎


In custom multi-stage pipelines

KPI-Specific Models

✔︎


Modules tuned to business KPIs

✔︎


Modules tuned to business KPIs


Model routing


Model routing

✔︎


In custom implementations
(e.g., MoE-RAG)

✔︎


In custom implementations
(e.g., MoE-RAG)

Automated ontology generation

Automated ontology generation

✔︎


Auto-generated &
easy to edit

✔︎


Auto-generated &
easy to edit


No native, persistent ontology feature


No native, persistent ontology feature


No native, persistent ontology feature


No native, persistent ontology feature

Research Areas

Research Areas

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

  • Continuously improves relevance, judgment, and workflow fit

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!

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