Read everything. Miss nothing
The engine underneath Parsewise reads every page of every document and turns them into one structured record. Each value links back to the page it came from.
0123of 412 claims
BI-30872Surgery recommended
BI-31455Surgical opinion pending
BI-29017Spinal surgery sought
0123of 412 claims
BI-30872Surgery recommended
BI-31455Surgical opinion pending
BI-29017Spinal surgery sought
The way business users work with unstructured documents has not changed for decades.
A structured world model
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.
- Word
- Excel
- PowerPoint
- Text
- LEDES
- Audio
- Image
- Video
Every file in the corpus, in any format.
A persistent representation of everything known about the task.
Which open claims name the same contractor?
Lowfield Architects and two other open files name the same contractor and allege the same defect.
Three principles
Exhaustive
Every page in the corpus is read and cross-checked against every other page, not just the ten closest snippets.
Self-learning
Corrections and reviews train the engine directly, not a prompt. Accuracy compounds with use.
Accountable
Every answer cites the exact pages it came from, so every number can be defended.
A sample reads the big claims. The risk starts in the small ones
Reviews sample the largest reserves, and search only finds the words it looked for. A claim that is changing course is still small, and its notes rarely say it the same way twice.
Sampled review41 of 412 claims read
Claims by reserve today
01 of ?escalating claims found
- BI-30872Not in sample0 of 37 pages
- BI-31455Not in sample0 of 29 pages
- BI-29017No matchrecommends a spinal surgical opinion1 of 48 pages
Parsewise Data Engine412 of 412 claims read
Claims by reserve today
3 of 3escalating claims found
- BI-30872a surgical review has been recommended37 of 37 pages
- BI-31455pending surgical opinion29 of 29 pages
- BI-29017recommends a spinal surgical opinion48 of 48 pages
Reads every page
Agentic searchRAG-styleSemantic retrievalFive systems, one engine
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
Surgery advised
Back off work
Reserve +£22kRL 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
Sources for BI-30872Escalating
Orthopaedic reportp. 3
Adjuster notes, 2023p. 7
Employer emailp. 1
Enterprise scale
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
248,310 pages
0 min1 batch retried42 min
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
AccuracyGeneral modelKPI model
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
Help us build the engine
If you have deep experience in any of these areas, or one close to them, we'd like to hear from you. We're always hiring great engineers.
