Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation
Debanshu Das (Google), Lavi Nigam (Google), Sunil Kumar Jang Bahadur (Google), Gopala Dhar (Google)
Architectural Patterns & Composition
Summary
A compound AI system that enforces brand consistency in generative video production through retrieval-based brand DNA extraction and an adversarial multi-agent QC loop, improving brand compliance from 42% to 89%.
Description
Recent advancements in generative video models demonstrate high visual fidelity, yet their integration into enterprise environments is restricted by temporal inconsistencies and severe brand misalignment. Current monolithic architectures struggle to enforce rigid brand constraints, frequently hallucinating unapproved visual assets. We introduce Genflow, a Compound AI System designed to enforce brand consistency in generative media production. Our architecture integrates a retrieval-based 'Brand DNA' extraction module to parameterize generation according to established corporate identity guidelines. Furthermore, we implement an Adversarial Multi-Agent Quality Control (QC) loop. Instead of a single-pass generation, this pipeline employs evaluator agents to iteratively critique generated frames against the extracted parameters, prompting generator models to refine outputs until a deterministic consensus is reached. By transitioning to a multi-stage, self-correcting pipeline, Genflow improved the yield of brand-compliant video generations from 42% to 89%, establishing a robust framework for scalable, enterprise-grade generative systems.
