The system, in plain terms.
An AdTech startup's operations team spent hours daily curating advertising deals across multiple platforms, 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.
What needed to be solved.
Developed an AI agent system that automates deal curation across multiple advertising platforms, reducing manual work from hours to minutes.
- Understanding and adapting to different platform requirements
- Handling authentication and rate limits across platforms
- Ensuring data consistency across platform APIs
- Providing visibility into automated actions
“AI agents excel at tasks requiring adaptation to multiple contexts and formats.”
What we set out to do.
- 01Automate deal creation across 5+ advertising platforms
- 02Reduce deal creation time from 2+ hours to <10 minutes
- 03Maintain 99%+ accuracy in platform-specific formatting
- 04Enable auditing and rollback capabilities
- 05Scale to handle 100+ deals per day
How we built it.
Understanding and adapting to different platform requirements — Built AI agents with platform-specific knowledge bases and validation rules, using LLMs to intelligently adapt content
Handling authentication and rate limits across platforms — Implemented robust OAuth management and intelligent request throttling with queue-based processing
Ensuring data consistency across platform APIs — Developed comprehensive validation framework with pre-submission checks and post-deployment verification
Providing visibility into automated actions — Built detailed logging and audit trail system with ability to review and rollback automated changes
From 2.5 hours
Deal creation time reduced from 2.5 hours to 8 minutes
What we used.
What changed in production.
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
Lessons from shipping it.
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.