Drug Trial Analysis AI
See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent
Advanced Multi-Agent System
BOFU: Production LangGraph with Router → Specialized Clinical Agents → Aggregation
1. Select Analysis Scenario
2. Monday's Prompt
3. Run Multi-Agent Workflow
MCP Tool Use Pattern
TOFU: LLM calls clinical trial tools via Model Context Protocol - see real tool execution
Automation Task
User query: 'What's the primary endpoint for Novo Nordisk's Phase 3 diabetes trial NN1234?' → LLM decides tool: extract_protocol_data() → Executes with NCT number → Returns structured protocol data → LLM formats answer
Terminal ready. Run simulation to begin.
RAG-Based Clinical Trial Search
MOFU: Search 100+ trial protocols with vector similarity
Search Query
"Search Historical Endpoint Patterns"
Embed query → Search 100+ protocol vectors → Retrieve top 5 matching protocol sections → LLM generates answer with specific trial citations
RAG Vector Search Network
Search Results
Run visualization to see results
Multi-Tool Clinical Trial Orchestration
MOFU: Multiple clinical tools working together automatically
ROI Analysis
Orchestrator calls: ClinicalTrials.gov Scraper (25 trials) → Protocol Analyzer (extract changes) → Endpoint Comparator (benchmark) → Safety Signal Detector (flag risks) → Report Generator (daily email) → Alert Tool (Slack notifications)
Time to Complete
Cost per Analysis
Data Points Analyzed
Accuracy Rate
Reports per Day
Error Rate
Ready for Production Drug Trial Analysis System?
We'll build your custom clinical trial intelligence system: MCP tools (TOFU - try ClinicalTrials.gov queries) → RAG search (MOFU - search 100+ protocols) → Multi-tool (MOFU - daily automated monitoring) → Multi-agent (BOFU - production LangGraph with PlannerAgent, ExecutorAgent, MetaAnalysisAgent). From demos to deployment in 8-12 weeks.
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