LLM and MCP based Automated Deal Pricing Negotiation using Multi Modal Margin Forecasting and Pricing Scenario Simulation

Volume 16, Number 5

LLM and MCP based Automated Deal Pricing Negotiation using Multi Modal Margin Forecasting and Pricing Scenario Simulation

Authors

Chirag Soni ¹, Swati Shah ², Avi Reddy ², Mahesh Toro ², Rishabh Rishi Sharma ² and Kiran R ², ¹ PayPal, India, ² PayPal, USA

Abstract

Enterprise deal negotiation continues to present persistent challenges in modern business environments. The process itself remains highly manual, relying majorly on individual expertise rather than on broad, data-driven analysis. This approach becomes increasingly untenable as the volume and complexity of pricing scenarios grow, and as organizations face heightened competitive and operational pressures. Negotiators often base decisions on limited historical pricing or isolated financial data, overlooking emerging factors such as market mood, regulatory changes, customer willingness to pay, and peer benchmarking. The proliferation of AI agents has opened new opportunities for automating complex business processes. This paper presents our work on enhancing end-to-end deal negotiation through the integration of multiple AI systems via a Model Context Protocol (MCP) server. Our approach combines traditional machine learning with large language models to provide multi-modal margin forecasting and pricing scenario simulation, which serve as critical inputs for negotiation decisions. We demonstrate how consolidating financial health assessment, market sentiment analysis, pricing intelligence, and margin forecasting through a unified MCP framework can significantly improve negotiation outcomes while reducing cycle times. The system addresses key challenges in sales operations where human negotiators often miss critical data points due to time constraints and information silos across departments.

Keywords

Pricing Optimization, MCP Server, GenAI, LLMs, Artificial Intelligence, Multi Modal Forecasting