Cast your mind back to when ChatGPT first went mainstream in late 2022. Back then, AI tools were confined to singular modalities – text in, text out. You’d type a prompt and receive a written response. Image generators operated similarly in their own domains – text in, image out.

Today, it looks very different. Multi-modal AI systems are ubiquitous, processing, understanding, and generating multiple types of content, such as text, image, audio, and even video. 

Instead of separate tools for each media type, today’s AI can see what you show it, listen to what you say, and respond in whatever format makes sense.

So, how does multi-modal AI work? And what are some key examples of multi-modal AI that we can learn from?

What is Multimodal AI?

Multimodal AI refers to artificial intelligence systems that can process, understand, and generate multiple types of data – text, images, audio, video, and more – often simultaneously. 

While once viewed as the ‘next frontier,’ multi-modal AI has become somewhat of a benchmark for frontier generative AI models. 

According to Global Market Insights, the multimodal AI market was valued at $1.6 billion in 2024 and is projected to grow at a remarkable CAGR of 32.7% through 2034. This explosive growth reflects the rapid evolution and adoption of these versatile systems. 

Gartner’s research adds weight to this trend, predicting that 40% of generative AI solutions will be multimodal by 2027, up from just 1% in 2023.

What makes multimodal AI so valuable is its ability to mimic human-like understanding. 

We naturally process the world through multiple senses, all while integrating this information to form a comprehensive understanding of our environment. Multimodal AI aims to replicate this integration of different sensory inputs.

In practical terms, this means an AI that can:

• Analyse an image and provide a detailed text description
• Generate images based on text descriptions
• Understand spoken instructions and respond with both voice and visual content
• Process a video and extract key information in text form
• Interpret documents containing both text and images
• Create videos from text prompts

The technological foundation has been building for years, but recent breakthroughs in model architecture, computing power, and training techniques have accelerated progress dramatically. 

What was theoretically possible but practically limited in 2020 has now become commercially viable and increasingly accessible.

The Major Players and Their Multimodal Offerings

The race to dominate multimodal AI has intensified dramatically over the past year, and now, most frontier models from developers such as OpenAI, Anthropic, Meta, Gemini, Mistral, ByteDance and Tencent in China, offer multimodal functions. 

We should preface that multimodal AI models can take two forms that we’ll later elaborate on: unified and modular. 

Unified models are ‘true’ multi-modal models, where one integrated model handles the different formats. Modular approaches rather ‘assemble’ different models together.

Google’s Gemini

Google jumped into the multimodal game with Gemini, first released in late 2023. Unlike some of its competitors, Gemini was built from scratch to handle different types of data together – a unified multi-modal model. 

What makes Gemini interesting is how it connects the dots between what it sees and what it knows. Show it a photo of your fridge contents alongside a text prompt asking for recipe ideas, and it doesn’t just identify the ingredients – it actually understands how they might work together in a meal.

The latest version, Gemini 2.0 and Flash, focuses on speed without sacrificing too much quality. This makes it actually usable for real-world applications where AI responses ideally need to be near-instant. 

OpenAI’s Multimodal Ecosystem

OpenAI has created several specialised tools that excel in different areas:

• ChatGPT with Advanced Voice Mode transforms how we interact with AI through conversation. Released in mid-2024, it goes well beyond mechanical voice commands to enable fluid, natural discussions. Early testers report being able to interrupt mid-sentence, switch languages, and even get the AI to perform eerily accurate impressions of celebrities and fictional characters.
• DALL-E remains the benchmark for text-to-image generation for many users. Now in its third iteration, it’s capable of generating remarkably detailed and accurate images from text prompts, handling everything from photorealistic portraits to abstract concepts and complex scenes.
• Sora might be OpenAI’s most ambitious project yet – a text-to-video model that can generate up to a minute of coherent, detailed video from a simple text description. 

ChatGPT’s new general-purpose flagship (as of mid-2025), GPT-4o, is a unified multi-modal model. OpenAI intends to integrate all media formats into a singular, integrated platform. 

Anthropic’s Claude

Claude has evolved into a highly useful multimodal assistant, particularly for knowledge workers who deal with documents and images regularly. 

While it doesn’t integrate image generation, it shines when analysing visual content in context – it can examine a complex chart, understand how it relates to your conversation, and provide insights that connect the visual data to your specific questions. 

Claude’s Artifacts feature also enables the drawing of graphs and charts that other generative AI models are yet unable to do natively. 

Above: Claude can work with diagrams, charts, and other structured images. 

Meta’s Multimodal Initiatives

Meta has been quietly building an impressive portfolio of multimodal technologies. 

Their SeamlessM4T model back in 2023 handled something particularly challenging – translating between speech and text across multiple languages while preserving the speaker’s voice characteristics.

Above: SeamlessM4T is a multilingual multimodal machine translation model supporting some 100 languages.

More visibly, Meta’s Ray-Ban smart glasses demonstrate how multimodal AI can work in wearable tech. 

The glasses combine voice recognition with visual processing to identify what you’re looking at and respond to your questions about it – a tantalising glimpse of how multimodal AI might become more integrated into our daily lives

Above: In the future, multi-modal AI will integrate more with our own senses. 

International Multimodal Projects

Beyond the US tech ecosystem leaders, smaller companies are carving out niches in the multimodal space. 

Runway AI has focused on creative tools for filmmakers and designers. Twelve Labs has specialised in sophisticated video analysis with its Marengo and Pegasus models.

Chinese tech companies are making impressive strides as well, with Baidu planning to release its Ernie 5 multimodal model, and companies like Alibaba integrating multimodal functions into their e-commerce platforms.

What would have essentially seemed like science fiction just 18 months ago is now available through accessible APIs and development platforms, placing these powerful systems within reach of businesses of all sizes.

How Multimodal AI Works In-Depth

Multimodal AI systems unify completely different types of information – images, text, audio, video – into a single understanding. But how exactly do they accomplish this?

Converting Inputs Into Numbers

At its core, multimodal AI first converts all types of data into numerical representations that a computer can process. This is enacted by specialised neural networks called encoders.

When you feed an image into a multimodal system, its image encoder breaks the picture into thousands of tiny patches. 

Each patch gets analysed by multiple layers of artificial neurons that detect progressively complex features – first edges and colours, then textures, then objects, and finally high-level concepts. The final output is a set of numbers (vectors) that represent what’s in the image.

Similarly, when you input text, a text encoder first splits your words into pieces (tokens), looks up each piece in its vocabulary, and processes them through attention mechanisms that track relationships between words. Again, the output is a set of numerical vectors.

Audio, video, and other data types undergo similar transformations through their respective encoders. Each encoder produces vectors in high-dimensional space – think hundreds or thousands of dimensions – where similar concepts are aligned. 

It’s essentially translating everything into a universal language that the AI understands – the AI learns to convert different expressions into the same underlying concept.

Aligning Concepts

Next, multimodal systems align these different vector spaces so that related concepts across modalities map to similar regions. This alignment is what allows the system to understand that the word “cat,” an image of a cat, and the sound of a meow all refer to the same concept.

To understand this, we need to rewind to the model training process. During training, the system is shown millions of paired examples – images with captions, videos with transcripts, etc. It learns to adjust its encoders so that related items across different modalities produce similar vectors.

For example, after proper training, the vector representation of the word “sunset” is placed in mathematical proximity to the vector representation of images showing sunsets. This allows the system to connect concepts across modalities.

During training, the system learns from these examples through several techniques:

• Contrastive learning forces the system to distinguish related cross-modal pairs from unrelated ones
• Masked prediction tasks have the system predict missing parts of one modality using information from another
• Alignment techniques ensure that representations from different modalities are compatible

This training process involves trillions of calculations across thousands of specialised AI chips, often running continuously for weeks or months. 

In many ways, it’s similar – even analogous – to how humans learn. When you teach a child about a ball, for example, they eventually build mental connections between the word “ball” and the round toy they play with. 

Multimodal AI builds similar connections, just mathematically rather than neurologically.

Cross-Modal Reasoning

Once everything is encoded in compatible numerical formats, multimodal systems use cross-attention mechanisms to reason across different types of information.

Cross-attention is essentially a lookup operation: it allows representations from one modality to “query” representations from another. 

When you ask a multimodal AI about something in an image, the encoded text of your question can directly attend to relevant regions of the encoded image.

For instance, if you ask “What colour is the dog’s collar?”, the system:

1. Encodes your text question into vectors
2. Processes these vectors to identify the key elements (dog, collar, colour)
3. Uses cross-attention to locate the “dog” in the image representation
4. Further focuses attention on the “collar” region
5. Extracts colour information from that specific part of the image
6. Generates an answer based on all this integrated information

Cross-modal reasoning occurs across multiple layers in the neural network, allowing the system to handle increasingly complex relationships between different types of data.

Unified vs. Modular Systems

As we noted earlier, current multimodal systems take one of two forms:

• Unified architectures: Systems like Google’s Gemini and GPT-4o process all types of data through shared neural network layers. This enables deeper integration but requires enormous models to maintain performance across all modalities.
• Modular designs: Early versions of GPT-4 used specialised components for each modality connected through carefully designed interfaces. This offers flexibility and can leverage existing specialised models, but some subtle cross-modal connections may be overlooked.

The AI industry is trending toward more efficient, unified designs. Recent advances like sparse mixture-of-experts (MoE) models allow systems to activate only the relevant parts of their networks for specific inputs, dramatically improving efficiency.

Real-World Applications of Multimodal AI

So where is multimodal AI actually delivering value right now? The examples are nearly limitless, but here are the main industry impact areas:

• In healthcare, radiologists are using systems that integrate MRI and CT imagery with patient records and lab results, catching correlations that might be missed when examining each separately. 
• For retail, multimodal AI has transformed product discovery. Major platforms like Amazon and Shopify now let customers search with images plus text refinements like “but in blue” or “made of leather,” bridging the gap between what we can see and what we can describe.
• Manufacturing has embraced multimodal quality control, with companies like Tesla and Toyota combining visual inspection systems with acoustic analysis to detect defects invisible to the human eye but detectable through abnormal operating sounds.
• Content creation teams now leverage tools that generate coordinated text, images, and video assets that maintain consistent branding and messaging. You might have seen or used them on TikTok, Instagram, or at work. 
• In customer service, multimodal chatbots can now understand photos of broken products alongside text descriptions, dramatically improving first-contact resolution rates for technical support issues.
• In agriculture, farmers are using multimodal systems that combine satellite imagery, soil sensor data, and weather forecasts to optimise irrigation and fertilisation. These systems can detect early signs of crop disease by correlating visual plant changes with environmental conditions, significantly reducing crop losses.
• Robotics has made tremendous leaps through multimodal AI, with warehouse and factory robots that can see objects, understand verbal instructions, and sense their physical environment. This enables them to navigate complex spaces, manipulate diverse objects, and collaborate safely with human workers.

Multi-modal AI has become the norm rather than the exception among frontier models, and we already take it for granted. 

From analysing photos from directly within the AI interface to chatting with AI voice assistants, models are becoming more deeply integrated across the senses.

Developing Multimodal AI Products With Aya Data

Multimodal AI is changing how machines understand and interact with the world, combining different types of data to create incredibly versatile systems. For businesses looking to implement this technology:

If you’re looking to build multimodal systems, as always, high-quality data forms the foundation of successful multimodal AI implementation. 

At Aya Data, we provide exactly this – properly annotated datasets created by domain experts who understand the critical relationships between text, images, audio, and video in specific contexts.

Our team has helped clients across industries build exceptional data foundations for successful multimodal AI projects. 

Whether you’re just starting to explore multimodal applications or looking to improve an existing system, we can provide the data expertise you need to build AI that truly understands your business.

Ready to explore what multimodal AI can do for you? Contact us today to discuss your project.