AdTech Startup
AI-Powered Multi-Platform Integration
Overview
An AdTech startup's operations team spent hours daily curating advertising deals across multiple platforms (Google, Facebook, Amazon), copying data, formatting content, and ensuring consistency. This manual process was error-prone, slow, and limited their ability to scale. They needed an intelligent automation solution that could understand deal requirements and orchestrate creation across platforms.
We built an AI agent system that uses LLMs to understand deal parameters, automatically format content for each platform's requirements, and coordinate multi-platform deployment. The system handles edge cases intelligently, validates all data before submission, and provides detailed logs for auditing.
The platform now automates 90% of deal creation tasks, allowing the operations team to focus on strategy and optimization rather than manual data entry.
Objectives
Automate deal creation across 5+ advertising platforms
Reduce deal creation time from 2+ hours to <10 minutes
Maintain 99%+ accuracy in platform-specific formatting
Enable auditing and rollback capabilities
Scale to handle 100+ deals per day
Challenges & Approach
Challenge
Understanding and adapting to different platform requirements
Solution
Built AI agents with platform-specific knowledge bases and validation rules, using LLMs to intelligently adapt content
Challenge
Handling authentication and rate limits across platforms
Solution
Implemented robust OAuth management and intelligent request throttling with queue-based processing
Challenge
Ensuring data consistency across platform APIs
Solution
Developed comprehensive validation framework with pre-submission checks and post-deployment verification
Challenge
Providing visibility into automated actions
Solution
Built detailed logging and audit trail system with ability to review and rollback automated changes
Outcomes & Impact
Deal creation time reduced from 2.5 hours to 8 minutes
90% of deals created with zero manual intervention
99.2% accuracy rate in platform-specific formatting
Successfully processing 150+ deals per day
5x increase in operational capacity with same team size
Key Learnings
AI agents excel at tasks requiring adaptation to multiple contexts and formats. We learned that providing agents with rich context about platform requirements dramatically improves output quality. However, comprehensive validation is critical—we implemented multi-stage verification to catch errors before they reach production platforms.
User trust in automation grows gradually. We built extensive visibility and control features, allowing operators to review automated decisions. Over time, as they gained confidence in the system's accuracy, they reduced manual reviews. The ability to audit and rollback changes proved essential for building this trust.
Technology Stack
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