Make AI Agent Automates Ad Creative Tagging for TripleTen, Processing 1,000+ Assets Per Year.

TripleTen, an online tech education provider, has automated its entire ad creative lifecycle using three interconnected scenarios on Make, including a Make AI Agent powered by Google Gemini. The system now automatically tags more than 1,000 creative assets per year across 20 parameters and connects them to real performance data in seconds, according to a case study shared by Make.

The company runs a high-volume performance marketing operation, testing hundreds of creative assets per month across Meta, YouTube, TikTok, and Google. Before automation, creative teams spent weeks manually tagging assets and linking them to performance metrics.

The automated workflow eliminates manual tagging labor. Google Gemini processes each creative asset, assigns metadata tags across 20 dimensions such as format, messaging angle, visual style, and target audience, then pushes the tagged data into a centralized system where it links to live campaign performance from multiple ad platforms.

"Given all the results, we will definitely use Make for all our other tasks connected with automation," said Max Epifanov from TripleTen in the announcement.

TripleTen built three interconnected Make scenarios to handle the full creative lifecycle. The first scenario ingests new creative assets as they are produced. The second scenario triggers the Make AI Agent, which uses Google Gemini to analyze and tag each asset. The third scenario syncs the tagged assets with campaign performance data from ad platforms, creating a unified view of creative effectiveness.

This architecture separates ingestion, tagging, and reporting into modular components. If one platform's API changes or a new ad channel is added, the team can update a single scenario without rebuilding the entire system.

Make AI Agents, recently rebuilt inside the scenario builder for greater transparency and control, allow users to see exactly how the AI processes data at each step. TripleTen's implementation shows how AI agents can handle structured data transformation tasks at scale.

Before automation, TripleTen's marketing team manually reviewed each creative, assigned tags based on subjective judgment, and cross-referenced performance data in spreadsheets. This process created delays between creative launch and actionable insights. By the time teams identified winning creative patterns, campaigns had already spent significant budget on underperforming variants.

The automated system tags assets immediately after upload and updates performance metrics in near real time. Marketers can now identify high-performing creative patterns within hours of launch, reallocate budget faster, and iterate on winning formats while campaigns are still active.

The case study follows Make's broader push into AI-powered automation. The platform recently launched Claude Skills and MCP Server to let AI build automations directly, and added guardrails through MCP Toolboxes to control agent permissions.

The TripleTen implementation demonstrates how AI agents can move beyond conversational interfaces into structured workflow automation, particularly for teams managing repetitive data transformation tasks at scale.