Imagine a pendulum swinging between two extremes: razor-sharp search accuracy and those delightful “aha!” moments of unexpected discovery. What if you could capture both — precision and serendipity — while simplifying AI search?

That’s exactly what we’re delivering with the launch of Cohere Multimodal Embed 3, and we can’t wait for you to dive in.

Discover multimodal AI search

Multimodal AI search is shaking up how businesses handle search and discovery. By blending the power of text and images, enterprises can now unlock a deeper understanding of user intent — and make every search more intuitive and impactful than ever.

By analyzing multiple modalities, you can:

Deliver more natural and intuitive user interactions
Provide highly relevant search results tailored to each user's needs
Significantly reduce the time users spend searching for information
Remove the hassle of managing multiple embedding databases

The recipe for success

The formula for transforming enterprise search is simple:

Start with a robust foundation of diverse data sources including text and images of complex reports, product catalogs, and design files.
Convert text and images into embeddings within a single database with the power of our cutting-edge multimodal Embed model. Try it in the Cohere playground.
Watch as your search and retrieval results soar to new heights of relevance and user satisfaction.

From highly personalized recommendations to sophisticated diagnostics, multimodal embeddings used for AI search and retrieval can help reshape and optimize customer experiences, business operations, and deliver more insights faster.

Take retailers, for example. They're using multimodal AI to bring visual search to ecommerce, making the whole experience feel more natural and intuitive. McKinsey partner Louise Herring recently shared, “One exciting development I’ve seen is in luxury retail, where AI is revolutionizing the discovery and inspiration phases of the customer journey.” By blending different types of data, businesses can deliver smarter search results and recommendations — a competitive edge that’s only set to grow.

What’s there to think about?

As multimodal AI search moves into real-world enterprise applications, it’s important to consider several key factors. These include:

Computational cost of processing data from multiple sources at scale
Need for even larger, more diverse datasets for training
New evaluation methods to measure performance on cross-modal tasks
Reducing potential for biases. For example Embed 3 prioritizes the meaning behind data, without biasing towards a specific modality, to ensure the most relevant results.

Consider choosing an AI provider that will collaborate with you and help tackle data challenges together. Our solutions architects and forward-deployment engineers are passionate about solving tough challenges and partnering with customers to make real progress. Got a complex problem? Don’t hesitate to reach out — we’re here to help you make it happen!

The versatility of multimodal AI search opens the door for many applications across multiple fields and industries. Trends, like feature extraction to recognize visual objects, shapes, colors, or textures combined with personalization and real-time data retrieval and analysis, are shaping how businesses use multimodal embeddings — and the future looks bright. It’s yet another example of AI helping us do things better.

Explore what's possible in the Cohere playground.

This post was originally published as part of Cohere's monthly newsletter on Enterprise AI.

AI search goes multimodal