Feature Feedback 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 → Executes → Returns result
Terminal ready. Run simulation to begin.
RAG-Based Search
MOFU: Search 100+ feedback items with vector similarity
Search Query
"Search Historical Feedback"
Embed query → Search vectors → Retrieve similar chunks → Generate answer with citations
RAG Vector Search Network
Search Results
Run visualization to see results
Multi-Tool Orchestration
MOFU: Multiple tools working together automatically
ROI Analysis
Orchestrator calls: fetch(intercom, zendesk, in_app) → deduplicate() → score_sentiment() → calculate_impact() → generate_report() → send_alerts()
Time to Complete
Cost per Analysis
Data Points Analyzed
Accuracy Rate
Reports per Day
Error Rate
Ready for Production Feature Feedback 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. 40 hrs/week → 2 hrs/week, $312K/year saved, 24/7 monitoring, 10x faster issue detection.
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