Confidential
United States
Advertising Technology / Marketing AI / Creative Automation
Amazon Bedrock (Nova Canvas, Claude Sonnet, Claude Vision), AWS ECS Fargate, Amazon S3, Application Load Balancer, LangGraph, FastAPI, Docker, Google Gemini, Pillow
The client is an Advertising Technology company focused on data-driven marketing and creative automation. As demand for high-volume, platform-ready ad creatives grew, the company faced a critical bottleneck: manual design workflows could not scale to meet the speed and volume the market required. Avahi partnered with the client to design and deliver a production-grade, end-to-end GenAI pipeline on AWS that transforms simple text prompts into fully composed, safety-reviewed, and brand-compliant ad creatives. The result is a scalable, automated system that dramatically accelerates creative production cycles while enforcing rigorous compliance and brand consistency at every stage.
The client is an Advertising Technology and Marketing AI company specializing in providing marketing teams, designers, and campaign managers with intelligent tools that streamline the production and deployment of advertising content. Operating at the intersection of data, AI, and creative automation, the client serves advertising agencies, e-commerce brands, and social media marketing platforms that require rapid, high-quality creative output at scale.
As the advertising landscape grew increasingly competitive, the client faced mounting pressure to produce high-quality, platform-ready creatives faster and at greater volume. Their existing approach relied heavily on manual design workflows, where creative teams were responsible for producing, reviewing, and compositing ad assets by hand. This process was slow, expensive, and fundamentally difficult to scale, a critical disadvantage in an industry where speed-to-market directly impacts campaign performance.
Beyond velocity, the manual approach introduced significant risk. Ensuring that every creative met content safety requirements, maintained brand consistency, and remained compliant across different advertising platforms required constant human oversight. Even with dedicated reviewers, the probability of non-compliant or off-brand content making it through the process was real and growing as output demands increased.
The client also lacked a systematic mechanism for tracking creative provenance, no reliable way to verify where an asset originated, whether it had been tampered with, or if it met the standards required for distribution. As AI-generated content became more prevalent across the industry, this gap in traceability represented both a legal and reputational risk.
Without a scalable, automated solution, the client risked falling behind competitors, absorbing rising production costs, and exposing clients to compliance failures that could damage campaign outcomes and brand trust.
AWS provided the client with the infrastructure depth and AI capabilities required to support a production-grade generative AI workload. Amazon Bedrock gave the team access to best-in-class foundation models, including Nova Canvas for image generation, Claude Sonnet for LLM-based content classification, and Claude Vision for post-generation image review, all within a managed, secure environment that eliminated the overhead of standing up and maintaining model infrastructure independently.
Beyond AI services, AWS’s container and networking ecosystem enabled the scalability and reliability the pipeline demanded. ECS Fargate provided serverless container orchestration that scales automatically with workload, while Amazon S3 served as the backbone for secure asset storage and distribution. Application Load Balancer ensured consistent traffic routing and enabled the asynchronous design patterns needed to handle long-running creative generation requests. Together, these services gave the cleint a cloud foundation capable of growing with their business without introducing operational complexity.
The client needed more than a vendor; they needed a technical partner who understood both the complexity of enterprise AI architecture and the operational demands of a production advertising environment. As a premier-tier AWS partner, Avahi brought the cloud expertise, service-level depth, and implementation rigor necessary to design a system that would hold up in a real-world, high-stakes deployment.
Avahi’s ability to architect multi-model AI pipelines, seamlessly integrating AWS Bedrock models alongside third-party services like Google Gemini, demonstrated an uncommon breadth of capability. Rather than building around a single model or vendor, Avahi designed a service abstraction layer that made the pipeline model-agnostic and extensible, giving the customer the flexibility to swap or add models as the technology landscape evolved. This forward-looking architecture, combined with Avahi’s deep experience in containerized deployments and compliance-driven systems, made them the clear choice to lead the engagement.
Avahi designed and built a nine-step, production-grade GenAI pipeline that takes a simple text prompt from a marketing team member and outputs a fully composed, safety-reviewed, watermarked, and brand-compliant advertising creative, with no manual intervention required at any stage of the process.
The pipeline begins with input validation and content tier classification. Before any generative work is performed, the system evaluates the user’s prompt against a four-layer guardrail framework. The first layer applies deterministic, rule-based filters to flag explicit violations immediately. The second layer leverages Claude Sonnet via Amazon Bedrock to perform LLM-based semantic classification, identifying nuanced or context-dependent content risks that rule-based systems alone would miss. This combination of deterministic speed and AI-powered judgment ensures that only appropriate, platform-ready prompts advance through the pipeline.
Once a prompt passes safety review, it is normalized and enhanced to optimize image generation quality. Avahi implemented a multi-model generation layer that routes requests to either Amazon Bedrock’s Nova Canvas or Google Gemini, depending on the creative requirements, using a unified service abstraction layer that treats both models interchangeably. This design allowed the team to incorporate the strengths of multiple generation engines without creating brittle, model-specific code paths.
After images are generated, each asset undergoes a third layer of review powered by Claude Vision, which evaluates the output for content safety at the visual level, catching issues that text-based guardrails cannot detect. Assets that fail this review trigger automated regeneration, ensuring that only compliant creatives advance. Approved images are then passed to a configuration-driven brand asset compositing engine built with Pillow, which applies brand elements, layout rules, and platform-specific formatting consistently across every output.
The final stages of the pipeline embed an invisible watermark using steganographic techniques, providing cryptographic provenance tracking without visibly altering the creative. Final assets are uploaded to Amazon S3 for secure storage and distribution. The entire pipeline is orchestrated by LangGraph, a modular graph-based framework that manages state and transitions between each step, enabling resilient execution with built-in fail-open and retry logic to minimize downtime. The full system is containerized with Docker and deployed on AWS ECS Fargate behind an Application Load Balancer, providing autoscaling, consistent uptime, and asynchronous request handling.
The GenAI pipeline delivered by Avahi transformed the client’s creative production capabilities, replacing slow, error-prone manual workflows with a fully automated system capable of generating safe, branded ad creatives in a fraction of the time. Marketing teams gained the ability to produce high volumes of platform-ready content from simple text prompts, while campaign managers and compliance reviewers benefited from built-in, multi-layer safety enforcement that reduced the risk of non-compliant content reaching distribution.
The architecture’s modularity and LangGraph orchestration also improved system reliability, enabling graceful failure handling and automated recovery that minimized pipeline downtime. The invisible watermarking capability gave the client a meaningful traceability advantage—critical for enterprise-grade AI adoption in a regulated advertising environment. The following outcomes were documented at project completion:
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