Case study / Media buying × AI
Delivered and in productionAutomating media buying operations
For a media buyer, Epicflare translated trading methods into connected AI workflows and deployed the solution in production. Agents support planning, campaign setup and routine optimization, while media buyers retain control over key decisions.
Media buying automation
Case study · English · PDF · 1 page
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The challenge
The engagement addressed manual work across media buying operations. The workflow needed to retain the buyer's trading rules and naming conventions, with opportunities to review and revise actions before they were applied.
The workflow
The workflow uses briefs, budgets, reporting, historical conversion data and existing campaign setups. The platforms covered are Google Ads, Meta Ads and AppLovin.
Planning
Agents propose plans using inventory, whitelists, audiences, themes and contexts, with CPM and CPA estimates.
Plan approval
The media buyer selects and revises a plan before the system writes changes. Business rules remain part of the workflow.
Campaign creation
Agents create line items, budgets and creatives using the buyer's bids, structure and naming conventions.
Optimization
Agents analyze performance and CPA, reallocate budgets and identify low-performing line items and creatives for removal.
Action approval
The media buyer reviews key actions and can revise them before implementation.
Update
Approved changes are written to the tools. New campaign results inform subsequent optimization cycles.
Brief & data
- Briefs
- Budgets
- Reporting
- Historical conversion data
- Existing campaign setups
Planning
Agents propose plans with CPM and CPA estimates
Plan approval
The media buyer selects and revises a plan
Human approval
Campaign creation
Agents create line items, budgets and creatives
Optimization
Agents analyze performance and CPA
Action approval
The media buyer reviews key actions and can revise them
Human approval
Update
Approved changes are written to the tools
Steps 4 to 6 repeat: New campaign results inform subsequent optimization cycles
Platforms covered
- Google Ads
- Meta Ads
- AppLovin
What Epicflare delivered
Epicflare translated trading methods into workflows, developed the agents and their connections, and integrated the solution into operations. The engagement was delivered and deployed in production. The client is presented anonymously.
Observed reductions in manual work time
| Activity | Observed range |
|---|---|
| Media and audience planning | 40–60% |
| Campaign setup | 50–75% |
| Routine optimizations | 20–35% |
Ranges vary by scenario. For planning, the comparison is between manual work time and time spent revising AI outputs. The optimization gain is approximate and is also linked to other process changes. No CPA improvement is claimed.
Which part of your media buying workflow needs attention?
Discuss the work your teams do today, the tools they use and the decisions they need to control. We can assess a relevant automation scope and define how to evaluate it.