Coheso Team
Coheso Team
Artificial Lawyer conducted an in-depth interview with Coheso CEO Ned Gannon, exploring the company's approach to AI, competitive differentiation, and vision for the future of in-house legal technology.
On Large Language Models in Legal
The interview delved into how Coheso leverages LLMs differently than other legal tech vendors. Key insights include:
Beyond Simple Chatbots
Ned explained that Coheso uses AI for more than answering questions. The platform employs AI throughout the workflow:
- Request understanding — Analyzing incoming requests to determine type, urgency, and routing
- Knowledge retrieval — Finding relevant policies, precedents, and documentation
- Response generation — Drafting initial responses for attorney review
- Workflow automation — Triggering appropriate processes based on request content
Enterprise Considerations
The conversation covered critical requirements for enterprise AI deployment:
- Security architecture that keeps client data protected
- Audit trails for compliance and accountability
- Human oversight at appropriate decision points
- Integration with existing legal technology stack
Why Coheso Will Be Effective
Ned articulated the factors that position Coheso for success:
- In-house focus — Purpose-built for corporate legal, not adapted from law firm tools
- Timing — Launched as generative AI reaches enterprise readiness
- Experience — Team has built successful legal tech products before
- Architecture — AI-native design enables deeper integration than retrofitted features
The CLM Connection
The interview explored Coheso's relationship with contract lifecycle management systems. Rather than competing with CLMs, Coheso complements them by:
- Capturing contract requests through centralized intake
- Routing requests to appropriate CLM workflows
- Maintaining visibility across all legal work, not just contracts
- Providing the "front door" that feeds into specialized systems
AI's Evolution in Legal
Ned reflected on how AI in legal has matured since his eBrevia days. Early applications focused on narrow tasks like contract extraction. Today's generative AI enables broader capabilities—but also requires more sophisticated deployment strategies.
This interview was originally published by Artificial Lawyer. Read the full interview →