Lawgorithm demo
Find the material that matters
Search and group a large document set by entity, topic and date without reading everything.
The challenge
Relevant material is found by reading, which does not scale to a large production.
The scenario
A specific entity's involvement must be traced across a large document set.
What goes in
- Indexed production
- The organised discovery set.
What you can ask
Illustrative prompts. Results depend on your own data and are not deterministic.
- “What material mentions this entity?”
- “Group these by topic.”
- “What happened in this date range?”
How Clearception approaches it
How the work is structured. Each step shows what its output is grounded in.
Resolve entities
AI-inferredIdentify people and organisations across documents.
Group and search
ObservedOrganise by entity, topic and date.
What you get
The shape of what comes back — not a promised result.
Grouped material
ObservedDocuments grouped by entity and topic with citations.
Why it matters
- Findable at scale
- Relevance does not depend on having read everything.
Related demos
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Take in and organise a discovery set
Deduplicate, classify and index discovery material on arrival, with provenance recorded.
4 minute walkthrough
- Document Intelligence
- Chain of Custody
- Evidence Trail
Lawgorithm
Build a timeline you can cite
Assemble dated events with the document supporting each one attached.
5 minute walkthrough
- Timeline Construction
- Document Intelligence
- Evidence Trail
Cortex Grid
Build a timeline from digital evidence
Assemble a chronology from fragmented digital sources with custody preserved throughout.
5 minute walkthrough
- Chain of Custody
- Timeline Construction
- Evidence Trail
Want to try this with your own work?
Open Lawgorithm and bring your own data. No signup is needed to read these demos.