Intelligent Yield Optimization Engine
Deploys a multi-agent AI system with 6 specialized agents that collaborate in real-time to analyze market conditions, forecast demand patterns, optimize floor prices, manage inventory allocation, and assess risks. The system provides transparent reasoning chains, human-in-the-loop approval workflows, and automated execution with configurable autonomy levels.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Agent Orchestration dashboard showing multi-agent coordination engine with real-time workflow stages (Market Analysis, Demand Forecasting, Price Optimization), individual agent status cards with success rates and latency metrics, and execution telemetry tracking API calls, LLM tokens, and errors.
AI Assistant interface providing natural language interaction with optimization agents, displaying Market Intelligence analysis with identified opportunities (NFL Playoffs demand surge, Auto Vertical growth, Competitor Gap), recommended actions with revenue potential, and available agent selection panel.
Reasoning Explorer providing transparent view into AI agent decision-making with chain-of-thought reasoning, showing completed reasoning chains for Market Intelligence, Demand Forecaster, and Price Optimizer agents with confidence scores, token usage, and detailed tool call execution traces.
Optimization Results dashboard presenting executive summary with key findings including +$47,000 projected revenue increase (+18.7%), high-performance execution metrics (99.4% success rate, 168ms P95 latency), 89% average confidence across decisions, and step-by-step process summary.
AI Agents
Specialized autonomous agents working in coordination
Yield Orchestrator
Complex yield optimization requires coordination of multiple specialized analyses that must execute in proper sequence with dependency management. Manual coordination is error-prone and cannot handle real-time market dynamics.
Core Logic
Coordinates all optimization agents through intelligent workflow management, handles task delegation, manages agent dependencies, synthesizes multi-agent outputs into unified recommendations, and ensures proper sequencing of market analysis, forecasting, and optimization stages. Monitors workflow status and handles exceptions.
Market Intelligence Agent
Publishers lack real-time visibility into competitive pricing landscapes, market trends, and optimal floor price benchmarks across their inventory segments.
Core Logic
Analyzes competitive landscape by fetching real-time market data from pricing APIs, performs competitor analysis across premium publishers, identifies pricing trends and market dynamics, and detects opportunities where inventory is underpriced versus market averages. Outputs actionable insights on optimal price positioning.
Demand Forecaster Agent
Static pricing cannot capitalize on demand surges from live events, seasonal patterns, or advertiser budget cycles. Publishers need predictive signals to optimize pricing ahead of demand changes.
Core Logic
Runs ML-based time-series forecasting models to predict demand patterns, identifies upcoming demand surges (e.g., NBA Playoffs, holiday advertising), quantifies expected demand changes with confidence intervals, and determines which inventory segments will be most affected. Provides actionable forecasts for pricing decisions.
Price Optimizer Agent
Setting optimal floor prices requires balancing revenue maximization against fill rate impacts across thousands of inventory segments, a task impossible to do manually at scale.
Core Logic
Calculates optimal floor prices using demand curve analysis and bid landscape optimization. Applies gradient descent algorithms to maximize yield while respecting fill rate constraints. Generates segment-level price recommendations with projected revenue impact, confidence scores, and detailed reasoning for each change.
Inventory Manager Agent
Suboptimal demand source prioritization and waterfall configuration leaves revenue on the table. Manual waterfall management cannot adapt to real-time demand source performance.
Core Logic
Optimizes inventory allocation across demand sources by analyzing fill rates, CPM performance, and demand source reliability. Recommends waterfall configuration changes, PMP prioritization, and allocation shifts to maximize overall yield while balancing direct and programmatic demand.
Risk Assessor Agent
Aggressive yield optimization can introduce risks including advertiser churn, compliance violations, and unsustainable fill rate impacts. Publishers need guardrails on AI recommendations.
Core Logic
Evaluates potential risks and compliance implications of proposed optimizations. Calculates risk scores considering fill rate impacts, advertiser relationship health, regulatory compliance, and historical performance. Flags high-risk recommendations for human review and suggests mitigation strategies.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
7 technologies
Architecture Diagram
System flow visualization