Case Study
Navari: Enterprise Agentic AI
From concept to enterprise-wide adoption in 10 months
Building an agentic AI platform that delivers complete working solutions, not just answers. How MCP-based tool execution transformed how scientists interact with AI.
!The Answer Problem
Enterprise AI tools were glorified search engines. Scientists would ask questions, get answers, then spend hours implementing them manually. The gap between insight and action was massive.
- Scientists spending 80% of time on implementation, not analysis
- Answers without context or actionable next steps
- No integration with internal data sources
- Each query starting from scratch with no memory
Agentic Execution
Navari doesn't answer questions—it completes tasks. Using Model Context Protocol (MCP), it connects Claude's reasoning to enterprise tools, executing multi-step workflows autonomously.
- MCP-based tool execution with 7+ deployed servers
- Direct integration with literature, regulatory, and internal data
- Context-aware responses understanding research domain
- Complete deliverables: applications, analyses, automations
Architecture
Navari uses a layered architecture separating reasoning from execution, enabling rapid tool development without touching the core AI layer.
Timeline
Key Lessons
Agentic AI requires thinking in terms of workflows, not responses
MCP provides clean separation between reasoning and tools
Word-of-mouth is the strongest adoption signal
Scientists want solutions that work, not answers to implement