Automation
AI Advertising Generation Engine
Generation and optimization of large numbers of targeted ad variants with measurable feedback on results.
Context
A large volume of targeted ad variants was needed with fast iteration across different segments.
Problem
Manual variant production did not scale, and performance feedback was not closed back into the process — conclusions were drawn by hand and with delay.
Constraints
Compliance with ad-platform requirements, quality control, measurable effect per variant.
Architecture
Variant generation → filtering by platform rules → launch → metric collection → pool re-optimization. The loop is closed.
AI layer
Creative and variation generation; the model is chosen by step cost and risk, with bulk generation on a cheap one.
Event model
Campaign metrics arrive as events and trigger re-optimization — which variants to scale, which to reject.
Integrations
Ad platforms, analytics and a creative store connected through a normalized layer.
Automation flows
Bulk generation, rule-based rejection, A/B distribution, iterative optimization by result.
Infrastructure
Generation queues, token budgets, idempotent campaign launches.
Observability
Tracing a variant through to performance metrics, generation cost per result.
Results
Generation scale without linear team growth; feedback closed into the process, not into manual review.
Lessons
Value is not in generation volume but in a measurable optimization loop; without closed feedback this is just expensive generation.