Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866916018100633600 |
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| author | Das, Debanshu Nigam, Lavi Bahadur, Sunil Kumar Jang Dhar, Gopala |
| author_facet | Das, Debanshu Nigam, Lavi Bahadur, Sunil Kumar Jang Dhar, Gopala |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation Das, Debanshu Nigam, Lavi Bahadur, Sunil Kumar Jang Dhar, Gopala Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems Multimedia I.2.0; I.2.6; I.2.10; I.2.11 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. |
| title | Genflow Ad Studio: A Compound AI Architecture for Brand-Aligned, Self-Correcting Video Generation |
| topic | Graphics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Multiagent Systems Multimedia I.2.0; I.2.6; I.2.10; I.2.11 |
| url | https://arxiv.org/abs/2605.16748 |