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.

翻译：近期生成式视频模型在视觉保真度方面取得了显著进展，但其在企业环境中的集成受到时间不一致性及严重的品牌偏差限制。当前的单体架构难以强制执行严格的品牌约束，时常产生未经批准的视觉资产。我们提出Genflow——一种旨在确保生成式媒体制作中品牌一致性的复合人工智能系统。该架构集成基于检索的"品牌DNA"提取模块，根据既定企业形象指南对生成过程进行参数化。此外，我们实施了一个对抗性多智能体质量控制（QC）循环。该流水线摒弃单次生成模式，采用评估智能体对生成的帧进行迭代式批判分析，将其与提取的参数进行对比，并促使生成器模型持续优化输出，直至达成确定性共识。通过过渡到多阶段自校正流水线，Genflow将符合品牌规范的视频生成有效产出率从42%提升至89%，为可扩展的企业级生成系统建立了稳健框架。