Investor Relations AI
See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent
Advanced Multi-Agent System
BOFU: Production LangGraph with Router → Specialized Agents → Aggregation
1. Select Analysis Scenario
2. Monday's Prompt
3. Run Multi-Agent Workflow
MCP Tool Use Pattern
TOFU: LLM calls tools via Model Context Protocol - see real tool execution
Automation Task
User query → LLM decides tool (extract_investment_thesis) → Executes → Returns thesis
Terminal ready. Run simulation to begin.
RAG-Based Investor Search
MOFU: Search 100+ investor docs with vector similarity
Search Query
"Search: Which investors focus on AI infrastructure?"
Embed query → Search vectors (100+ docs) → Retrieve top chunks → Generate answer with investor list
RAG Vector Search Network
Search Results
Run visualization to see results
Multi-Tool Orchestration
MOFU: Multiple tools working together automatically
ROI Analysis
Orchestrator calls: scrape_investor_portfolio(150 investors) → extract_investment_thesis(all) → score_investor_fit(all) → detect_changes(today vs yesterday) → generate_alerts(significant changes only)
Time to Complete
Cost per Analysis
Data Points Analyzed
Accuracy Rate
Reports per Day
Error Rate
Ready for Production Investor Relations System?
We'll build your custom system: MCP tools (TOFU) → RAG search (MOFU) → Multi-tool (MOFU) → Multi-agent (BOFU). From demos to deployment in 8 weeks. Monitor 150+ investors 24/7, detect opportunities in real-time, close deals faster.
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