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Rayon is a cloud-based design platform for architecture and interior design that enables teams to collaborate in real-time.
With millions of monthly searches and users in over 100 countries, Rayon needed a fast, multilingual search solution to keep up with explosive data growth. They turned to Meilisearch to power their semantic search and scale seamlessly.
“Meilisearch is very powerful for our needs, especially its semantic search, which effortlessly helps users find relevant objects, even without precise queries.”
Bastien Dolla, CEO and Co-founder at Rayon
Challenge: fast, intuitive, scalable search during rapid growth
With a rapidly growing user base in over 100 countries, Rayon needed to address critical challenges, including improving search functionality and supporting the rapid expansion of its database and usage while enhancing the overall user experience.
Challenge: Multilingual search for a global audience. Traditional keyword-based search systems usually require users to know the exact name of an asset, often in English. Rayon’s non-native English-speaking users make up a significant portion of its global audience, so the platform needed to support searches in multiple languages in order to eliminate friction.
Challenge: Rapid data expansion. Rayon’s platform offers access to over 4,000 assets, which represent real-world objects like furniture or textures. Users can also create and publish their own libraries, which leads to the rapid growth of the repository. With approximately one terabyte of new data added weekly, conventional tools simply couldn’t keep pace.
Challenge: Scaling demand. At the time of writing the platform handled 2 million searches a month and was projecting 20 million within the year. That trajectory called for a high-performance, scalable solution.
Solution: an AI-powered search experience for a global user base
Rayon turned to Meilisearch to address its challenges with multilingual search, scaling, and efficient data management. These were addressed by:
- Leveraging AI-powered semantic search for intuitive discovery: rather than relying on exact keywords, Meilisearch understands intent. For example, whether a user searches for “Scandinavian style” or “modern sofa with clean lines,” relevant results are quickly delivered.
- Enabling multilingual search: In addition to understanding the natural meaning of a query, Meilisearch supports non-English language searches, leveraging OpenAI embeddings to eliminate language barriers. For example, a search for 'chaise' in French or 'silla' in Spanish will return relevant chair designs, regardless of the language used.
- Scalable architecture for rapid growth: By indexing only design asset libraries and using token-based access control, Meilisearch efficiently manages data growth while ensuring stable performance and secure, authorized access.
- Open-source flexibility and Rust compatibility: The open-source origins of Meilisearch ensures that Rayon retains control over its search infrastructure, which provides them the flexibility to deploy their own instance if needed. Compatibility with Rust, the language Rayon is built on, mattered too: they have a strong Rust team, which made Meilisearch easier to integrate and operate.
Implementation
Rayon implemented an efficient indexing process to ensure users could quickly and accurately find the design assets they needed:
- Library processing and SVG generation: When users publish a library of design assets, Rayon processes the files and generates SVG images for each element. These images serve as visual representations of the assets, making it easier for users to identify what they are searching for.
- Embedding generation with OpenAI: Rayon uses OpenAI’s advanced AI models to generate embeddings for each design asset. These embeddings capture the semantic meaning of the asset, enabling Meilisearch to perform highly accurate searches based on natural language descriptions.
- Token-based access control: Meilisearch’s token system allows Rayon to maintain strict control over data visibility while still providing a seamless search experience.
Results
By solving key challenges, Meilisearch has become essential to Rayon’s mission, delivering faster searches, happier users, and a platform built for the future:
- Multilingual search for a global audience. Designers can now search for assets in their native language, eliminating the need for English-only queries.
- Faster, more intuitive search: Meilisearch’s high-performance engine enables users to find design assets in seconds using natural language, without needing exact names.
- AI-powered discovery. With OpenAI embeddings, Rayon's search goes beyond keywords, allowing users to find assets based on descriptions, even without precise terminology.
- Scalability for rapid growth. Meilisearch indexes 250,000 to 350,000 searchable items, covering both Rayon’s library and user-generated content. With 1TB of new data added weekly, it absorbs the growth in search volume as it scales from 2 million toward a projected 20 million searches a month.
Conclusion: solidifying Rayon’s position in cloud-based design tools
Meilisearch has become essential to Rayon's mission by solving multilingual access, scalability, and asset discovery challenges.
As Rayon continues to innovate, the team plans to explore new capabilities, such as visual search and deeper data and RAG integration, to evolve into a comprehensive platform for architects and designers.
Looking ahead, their vision goes beyond visual assets and drawings, aiming to integrate procurement details, material specifications, and other essential data into a truly comprehensive design hub.
Rayon runs this on Meilisearch Cloud. If you want to try the same semantic search setup, there is a 14-day free trial and no credit card is required.
Frequently asked questions (FAQs)
How does Rayon use Meilisearch?
Rayon powers search over its design asset library with Meilisearch, using OpenAI embeddings for semantic search so designers can find objects by describing them rather than naming them exactly. Tenant tokens control which libraries each user can see.
How does semantic search help multilingual users?
Embeddings capture meaning rather than exact wording, so a query in French or Spanish can match an asset described in English. For a platform whose users span more than 100 countries, that removes the requirement to know the English name of an object before you can find it.
How much data does Rayon index?
Between 250,000 and 350,000 searchable items across Rayon's own library and user-published libraries, with roughly one terabyte of new data added each week.
What is the difference between semantic search and keyword search here?
Keyword search matches the words in the query against the words in the document, so it needs the user to know the right term. Semantic search compares embeddings, which lets "modern sofa with clean lines" match assets that never use those words. Meilisearch can combine both as hybrid search.
