Project Overview

Goodhues Inc, a leader in virtual interior design solutions, sought to leverage AWS AI services to generate AI-driven images and mockups for interior decoration, background generation, and other tasks using fine-tuned image generation models on SageMaker. The proposed solution included a combination of AWS services across compute, storage, and machine learning. Avahi, an advanced tier AWS partner, was engaged to help Goodhues Inc on this transformative journey, focusing on developing a smart AI assistant and imagegeneration tools.

Challenges

Goodhues Inc faced several challenges in developing an AI-driven interior design solution:

• Complexity of Design Tasks: Generating accurate and aesthetically pleasing interior design images and mockups.
• Real-Time Processing: Providing near real-time image generation and updates based on user inputs.
• Data Management: Efficiently managing and processing large volumes of image data.
• Integration: Seamlessly integrating the AI models with existing systems and workflows.
• Scalability: Building an infrastructure capable of handling increasing demands and user requests.

Solution

Avahi proposed a comprehensive solution to address these challenges, focusing on developing proof of concepts (POCs) for several high-priority features requested by Goodhues Inc. The project was executed in four phases: Discovery & Planning, Design & Development, Validation & QA, and Executive Presentation & Handoff.

Key Deliverables

• POC Development: Building POCs for high-priority features such as chat with context, paint segmentation, upscaling, and designing an empty room.
• Real-Time Transcription: Developing a system to generate transcripts from audio recordings of online classes in near real-time.
• ETL Processes: Performing ETL on raw data to prepare it for model training, storing output text files in Amazon S3 for downstream processing by LLMs.
• Backend Pipeline Management: Creating an end-to-end pipeline to manage embeddings in all S3 documents on live data.
• Post-Processing Jobs: Running post-processing jobs based on confidence thresholds selected by Goodhues Inc.
• Validation: Ensuring accuracy, bias mitigation, and scalability using industry standard tools.
• Qualitative Review: An analysis report based on manual review to identify common issues and patterns.

Solution Architecture

The solution architecture leveraged various AWS services to implement the deliverables and ensure a robust, scalable platform.

Data Acquisition and Storage

• Amazon S3: Used for storing input documents, training images, and processed data. S3’s scalability and durability ensured efficient handling of large volumes of data.

Event-Driven Processing

• AWS Lambda: Deployed to run event-driven functions that process data and manage various stages of the pipeline. Lambda functions were used to fetch documents and images, create embeddings, and update/store outputs.

Machine Learning and AI

• AWS SageMaker: Utilized for developing, training, and deploying machine learning models for image generation and enhancement tasks.
• Amazon Bedrock: Used for scalable managed Generative AI and foundational model services, supporting the overall AI infrastructure.

API Management

• Amazon API Gateway: Provided a managed service to create, publish, maintain, and secure APIs. This facilitated seamless communication between the front-end applications and backend services, allowing easy access to generated images and design tools.

Monitoring and Logging

• AWS CloudWatch: Implemented for comprehensive monitoring and logging. CloudWatch provided real-time insights into system performance, user interactions, and the health of the deployed models.

Project Impact

Enhanced Interior Design Capabilities

The AI-powered image generation tools significantly improved Goodhues Inc’s interior design capabilities, allowing users to generate accurate and visually appealing designs in real-time. This led to increased customer satisfaction and engagement.

Operational Efficiency

By automating the design and image generation processes, Goodhues Inc saw a reduction in manual efforts and operational costs. The efficient data handling and machine learning models ensured timely and accurate delivery of design outputs.

Scalability and Flexibility

The architecture designed by Avahi ensured that Goodhues Inc’s platform was scalable, capable of handling increased data volumes and user demands. This provided flexibility for future enhancements and growth.

Improved Accuracy

The use of advanced machine learning models in AWS SageMaker and Amazon Bedrock enhanced the accuracy of image generation and design tasks, ensuring high-quality outputs that met user expectations.

Detailed Insights and Analytics

The deployment of AWS CloudWatch and other monitoring tools provided Goodhues Inc with detailed insights into system performance, model accuracy, and user behavior. These analytics were crucial for continuous improvement and optimization of the platform.

Conclusion

The collaboration between Goodhues Inc and Avahi led to the successful development of AI-powered image generation and interior design tools. Leveraging AWS services, Goodhues Inc enhanced its design capabilities, improved operational efficiency, and built a scalable platform ready for future growth. Avahi’s expertise in AWS solutions played a pivotal role in this transformation, demonstrating the power of Generative AI and machine learning in advancing interior design technology.