AI-Powered Marketing Campaign Analysis & Optimization
Deploys a coordinated team of 5 specialized AI agents that autonomously fetch campaign data, perform statistical anomaly detection using Isolation Forest algorithms, identify root causes through RAG-enhanced reasoning, generate prioritized optimization recommendations, and produce multi-stakeholder reportsβall within minutes instead of hours..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Campaign Diagnostics - Mission Control
Campaign Diagnostics - Executive Insights
Campaign Diagnostics - Audit Trail & Compliance
Campaign Diagnostics - Analysis Summary
AI Agents
Specialized autonomous agents working in coordination
Workflow Orchestrator Agent
Complex multi-agent workflows require intelligent coordination to ensure proper task sequencing, resource allocation, and result aggregation without bottlenecks or race conditions.
Core Logic
Powered by Claude 3.5 Sonnet with temperature 0.3 for deterministic behavior. Manages workflow state machine, delegates tasks to specialized agents via `delegate_task` and `broadcast_message` tools, aggregates results using consensus protocols, and synthesizes final outputs. Maintains conversation context and ensures guardrail compliance throughout execution.
Data Retrieval & Processing Agent
Marketing data is fragmented across multiple platforms (Google, Meta, LinkedIn) with different APIs, schemas, and rate limits, making unified analysis extremely difficult.
Core Logic
Utilizes GPT-4 Turbo with specialized tools: `google_ads_api`, `meta_ads_api`, and `database_query`. Normalizes disparate data schemas into unified format, handles pagination and rate limiting automatically, validates data integrity, and caches results for efficiency. Achieves 99% accuracy across 1,847+ completed analyses.
Statistical Anomaly Detection Agent
Manual review cannot identify subtle statistical anomalies in large datasets, leading to missed opportunities and undetected performance degradation until significant budget impact occurs.
Core Logic
Employs Claude 3.5 Sonnet with temperature 0.1 for precision. Runs Isolation Forest algorithm with STL decomposition for seasonal adjustment. Tools include `anomaly_detection`, `database_query`, and `vector_search`. Classifies findings by severity (Critical/High/Medium/Info) with confidence scores. Example detection: 67% conversion drop due to iOS 17 privacy changes.
Strategy & Budget Optimization Agent
Determining optimal budget allocation across channels and campaigns requires complex scenario modeling that considers ROI curves, diminishing returns, and cross-channel effects.
Core Logic
Powered by GPT-4 Turbo with temperature 0.4 for creative strategy generation. Leverages `budget_optimizer`, `forecasting`, and `vector_search` tools. Performs multi-armed bandit optimization and portfolio allocation modeling. Generates prioritized recommendations with expected ROI impact, confidence intervals, and implementation steps.
Multi-Stakeholder Report Generator
Different stakeholders (executives, analysts, technical teams) require different levels of detail and focus areas, but creating multiple report versions is time-consuming.
Core Logic
Uses Claude 3.5 Sonnet with 16,384 max tokens and temperature 0.5 for balanced output. Automatically generates three report variants: Executive (high-level KPIs and strategic insights), Technical (detailed methodology and statistical analysis), and Analyst (actionable recommendations with implementation guides). Includes visualizations and audit trail references.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization