Project Overview
RoboArt Labs sought to transform the apparel industry by enabling customers to generate AI-driven designs—such as Pixar-style characters and repeatable patterns—and seamlessly print them on custom products. With tight timelines and a need for reliable, scalable infrastructure, RoboArt Labs partnered with Avahi to build a proof-of-concept (POC) on AWS. By leveraging services like Amazon S3, Amazon Bedrock, and Amazon SageMaker, the team created a two-environment pipeline for both model training and realtime inference, delivering a fast and efficient generative AI workflow.
About the Customer
RoboArt Labs is an innovative startup focused on blending artistic expression with technology. Its platform empowers users to create personalized apparel designs using cutting-edge AI models. The company’s vision is to simplify design generation and printing, offering a creative, on-demand experience to customers around the globe.
The Problem
Developing a generative AI pipeline for custom art generation posed several challenges, including managing large volumes of training data, ensuring seamless model updates, and providing near real-time inference. RoboArt Labs also needed a straightforward way to handle user inputs, such as images and style prompts, without bogging down the development team in complex infrastructure tasks.
Why AWS
AWS offered a robust suite of cloud-native tools to meet RoboArt Labs’ needs quickly and securely. Amazon S3 enabled cost-effective data storage and retrieval, while Amazon SageMaker provided a fully managed environment for model training and tuning. AWS Lambda and Amazon Bedrock delivered a scalable way to serve real-time AI inferences, ensuring that the platform could handle bursts of user requests with minimal latency.
Why RoboArt Labs Chose Avahi
RoboArt Labs selected Avahi for its proven track record in implementing AI solutions on AWS. Avahi’s deep expertise with generative AI, combined with a hands-on approach, allowed the project to progress rapidly. By working closely with RoboArt Labs, Avahi translated complex technical requirements into a clear, achievable roadmap, ensuring that the solution aligned perfectly with the startup’s product vision and tight timelines.
Solution
Avahi implemented a two-environment architecture. The first environment, dedicated to model development, ingests image data into Amazon S3 and uses Amazon SageMaker to fine-tune and train models for Pixar-style character creation and repeat pattern generation. The second environment handles production inference through Amazon API Gateway, AWS Lambda, and Amazon Bedrock, returning highfidelity images on demand. AWS CloudWatch and IAM were integrated for monitoring, logging, and secure access control, enabling RoboArt Labs to scale confidently while maintaining data integrity.
Key Deliverables
- Data ingestion and storage with Amazon S3
- Model training and fine-tuning in Amazon SageMaker
- Real-time inference via Amazon Bedrock, AWS Lambda, and API Gateway
- Automated logging and monitoring with AWS CloudWatch
- Minimal documentation and a rapid validation window for POC feedback
- Knowledge transfer and post-handover guidance for future enhancements
Project Impact
RoboArt Labs can now rapidly transform user inputs—like photos and style prompts—into custom apparel designs. The solution significantly reduces development overhead, allowing the startup to focus on creative innovation and user experience. By centralizing data and leveraging managed AI services, RoboArt Labs is well-positioned to expand its generative AI features and scale its operations in the evolving custom apparel market.
