Pricing Optimization AI
See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent
Advanced Multi-Agent Pricing System
BOFU: Production LangGraph with Router → Pricing Specialists → Aggregation
1. Select Analysis Scenario
2. Monday's Prompt
3. Run Multi-Agent Workflow
MCP Pricing Tool Use Pattern
TOFU: LLM calls pricing tools via Model Context Protocol - see real tool execution
Automation Task
User query → LLM decides tool → Executes scrape_competitor_pricing() → Returns competitor pricing data
Terminal ready. Run simulation to begin.
RAG-Based Pricing Intelligence
MOFU: Search 100+ pricing docs with vector similarity
Search Query
"Search Historical Pricing Experiments"
Embed query → Search vectors → Retrieve pricing experiment docs → Generate insights on what worked/didn't work
RAG Vector Search Network
Search Results
Run visualization to see results
Multi-Tool Pricing Orchestration
MOFU: Multiple pricing tools working together automatically
ROI Analysis
Orchestrator calls: competitor_scraper(25 competitors) → demand_analyzer(last 7 days) → wtp_estimator(3 segments) → revenue_calculator(50 scenarios) → alert(if significant changes)
Time to Complete
Cost per Analysis
Data Points Analyzed
Accuracy Rate
Reports per Day
Error Rate
Ready for Production Pricing System?
We'll build your custom pricing optimization system: MCP tools (TOFU) → RAG search (MOFU) → Multi-tool (MOFU) → Multi-agent (BOFU). From demos to deployment in 8 weeks. $514K annual value (cost savings + revenue uplift).
2026 Randeep Bhatia. All Rights Reserved.
No part of this content may be reproduced, distributed, or transmitted in any form without prior written permission.