ADA Compliance with AI Assistance: A Multimodal RAG Approach

At Spatial Delivery, we’re harnessing the power of cutting-edge AI to make ADA compliance more accessible and intuitive for spatial designers. By leveraging multimodal Retrieval-Augmented Generation (RAG), we’re creating tools that simplify complex regulations and empower designers to focus on creativity without compromising inclusivity.

Challenges of ADA Compliance

The Americans with Disabilities Act (ADA) contains complex regulations for accessible design. Navigating dense legal documents and technical specifications can slow down creative workflows. Our vision is to empower designers with an intelligent assistant that delivers precise, actionable insights without compromising creativity.

Multimodal RAG: Bridging Text and Images

Our system processes ADA PDFs to extract text and images. Using Google’s VertexAI and Gemini models, it divides content into chunks and generates embeddings—numerical representations capturing the semantic meaning of content. These embeddings are stored in a vector database, enabling rapid similarity and comparison searches.

This multimodal approach offers significant advantages. While long-context querying excels at understanding detailed context, it struggles with processing speed and scale. Vector databases, on the other hand, can efficiently manage large datasets, providing near-instantaneous responses. The system combines textual summaries with relevant images to offer a richer, more intuitive understanding of ADA guidelines, making them easier to apply and comply with.

Initial Results

Initial tests showcase the system's potential with promising results. Users can ask questions like, “What are the requirements for accessible restroom signage?” or upload an image for analysis. The system provides clear, concise summaries of relevant rules paired with visuals, enabling designers to apply regulations efficiently and accurately.

Ongoing Research and Development

We’re currently comparing the performance of vector databases and long-context models to refine our system. Our goal is to develop a hybrid solution that combines the speed and scalability of vector searches with the deep understanding of long-context models. By integrating this solution into the front-end interface of our spatial computing platform, designers will have a handy tool to access ADA information in near real-time, keeping workflows smooth and uninterrupted.

A Vision for Inclusive Design

This research and learning marks an exciting step toward AI-driven accessibility in spatial design. By reducing the complexity of ADA compliance, we’re helping designers focus on what they do best - creating spaces that are inclusive and welcoming for everyone.