Collaborative AI for lawyers to review, research, and draft with precision
Agentic patent workflow platform for prior art search, claim strategy, and filing-ready patent drafting
Stage
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Founded
2023
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Based in
SE
IN
Pricing model
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Paid
Pricing
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Key features
250+ legal skills across 20+ practice areas
Integration with Word and Outlook
Jurisdiction-aware legal research with structured citations
Ethical walls and role-based access control
Full audit trails of all agent actions
Tabular review at scale
DMS integrations and document ingestion
Prior art search across 220 million patents from 108 jurisdictions and 100+ million research papers
Patent drafting with claims, description, abstract, and figures for US, EP, and Indian practice
Freedom-to-operate analysis with clearance reports and risk registers
White space analysis with filing trends and landscape analytics
Invalidity search mapping claim elements to prior art
Scope slider for exploring claim breadth
Provenance tracking on every claim element
Technical gap detection with proposed fixes at intake
Figure generation from specification without baseline drawing
Five approval gates with full audit trail
What makes it different
Encode firm expertise and processes as reusable Skills that adapt to each matter
Agent automatically selects the right skill for the task in plain language
Works in familiar tools like Word and Outlook
Used by 130,000+ lawyers at 2,000+ leading law firms globally
Enterprise security with ISO 27001, SOC 2 Type II, GDPR, and HIPAA compliance
Attorney-directed workflow with five decision gates where attorneys approve every substantive output, not just suggestions
Multi-agent, multi-LLM system with specialist agents on different models handling search, claims, description, and figures, with independent quality gate review
Seven integrated workflows in one platform with shared corpus of 220 million patents instead of separate tools delivered weeks apart
Outcome-based pricing per matter delivered rather than per-seat licensing
ISO/IEC 27001:2022 certified security with customer data never used for model training