AI Deal Intelligence Agent

Core Logic

## Approach The orchestrator agent operates as the central coordinator, decomposing deal requirements into specialized tasks, dispatching work to appropriate specialist agents, monitoring progress through workflow phases, resolving conflicts between agent recommendations, and synthesizing final consensus decisions. Uses GPT-4 Turbo with low temperature (0.3) for consistent task management and employs tools for workflow control, agent dispatch, and consensus building.

Core Logic

## Approach Leverages CRM integration to query Salesforce advertiser histories, searches market intelligence databases for trends and benchmarks, analyzes competitor bid patterns and market positioning, and detects emerging demand signals. Monitors real-time market pulse indicators including demand trends, pricing volatility, and competitive pressure scores to provide actionable intelligence for deal optimization.

Core Logic

## Approach Queries ad server inventory in real-time for availability across targeting criteria, forecasts campaign delivery using historical patterns with 95% confidence intervals, optimizes allocation across premium and programmatic inventory tiers, and calculates audience match scores. Evaluates inventory health metrics including utilization rates, viewability, fraud risk, and sell-through projections.

Core Logic

## Approach Runs Monte Carlo pricing simulations with 10,000 scenarios incorporating market factors, analyzes price sensitivity curves, calculates margin optimization strategies, and determines competitive positioning. Generates pricing recommendations with floor CPM, ceiling CPM, recommended CPM, and margin percentages. Provides win probability estimates based on historical data and current market conditions.

Core Logic

## Approach Validates deal terms against applicable jurisdictional regulations, checks for compliance with advertising industry standards (TAG, MRC), generates appropriate contract clauses based on deal structure and risk profile, and flags potential legal concerns. Uses conservative LLM temperature (0.1) for precision in legal determinations and maintains strict guardrails for sensitive content.

Core Logic

## Approach Analyzes historical negotiation patterns for similar deals and advertisers, applies game theory models to predict likely counter-offers and negotiation trajectories, generates strategic response recommendations, and identifies key differentiators and value propositions. Depends on insights from Market Intelligence and Pricing Strategist agents to inform negotiation positioning.

Core Logic

## Approach Extracts patterns from historical deal data across pricing, negotiation, inventory, audience, and timing dimensions. Applies pattern recognition to identify success factors and failure indicators, trains internal models for improved predictions, and maintains a knowledge base of learned patterns with application frequency and success rate tracking.

Core Logic

## Approach Scans real-time market conditions across inventory segments, tracks competitor activities including bids, wins, and campaign launches, generates instant optimization recommendations for pricing and inventory adjustments, and alerts on critical market changes requiring immediate attention. Operates with very low temperature (0.1) for consistent real-time decision making.

Core Logic

## Approach Utilizes advanced ML models for multi-horizon forecasting, generates scenario comparisons across conservative, balanced, and aggressive strategies, predicts advertiser behavior and campaign outcome probability, and provides confidence-scored predictions with supporting factor analysis. Produces deal health scores with component breakdowns for pricing, inventory, risk, and market fit.