AI assistants have a major weakness when it comes to video: they can't actually understand what's happening on screen. While large language models like ChatGPT, Gemini, and Claude excel at processing text and generating images, they struggle to extract meaningful information from video content. They might access a transcript if one exists, but the visual demonstrations, product details, emotional cues, and sequence of events remain largely invisible to them.

This limitation is about to become a much bigger problem. As AI assistants increasingly replace traditional search engines like Google as the first place people look for answers, vast libraries of video content risk becoming invisible to the AI systems that could help users find information within them. French startup Aive believes it has found a solution, and it just partnered with Nvidia to accelerate the effort.

Why Video Understanding Matters More Than Video Generation?

For years, the AI industry has focused on generating videos. Companies have invested billions in tools that create new video content from text prompts. But Aive's CEO and co-founder Olivier Reynaud sees a different frontier: making existing videos understandable to AI systems.

Consider a practical example. If someone asks an AI assistant "What's the best face cream?" today, the model will likely draw from product reviews, blog posts, and retailer websites. But what if the most detailed, credible explanation lives in a 10-minute product demonstration video? Without the ability to understand that video's content, the AI assistant can't access it.

"If I'm advertising a cosmetics product through a ten-minute video, today's LLMs mostly see the transcript, if it's available to them. They don't really understand what's happening visually. Our technology transforms that video into knowledge that AI can use," said Olivier Reynaud, CEO and co-founder of Aive.

Olivier Reynaud, CEO and co-founder, Aive

Reynaud noted that search behavior is shifting rapidly. More and more queries are happening through large language models rather than traditional search engines, making video accessibility a business opportunity rather than just a technical problem.

How Does Aive's Technology Extract Knowledge From Video?

Rather than generating new videos, Aive analyzes existing ones using its proprietary Multimodal Generative Technology, or MGT. The system extracts information from both the audio and visual content and converts it into machine-readable knowledge that large language models can understand and cite.

The company's approach combines more than 25 AI models to analyze different aspects of video content simultaneously. These models work together to identify scenes, objects, products, emotions, speakers, and other visual signals before structuring all that information into data that language models can process.

This is part of a broader shift in AI architecture. Multimodal AI systems, which can process text, images, audio, and video within a single unified model, represent a fundamental change in how AI perceives information. Rather than treating each data type separately, modern multimodal systems reason across all modalities at once, the way humans naturally do when reviewing multiple types of information simultaneously.

Steps to Make Video Content AI-Searchable

• Encoding: Each modality (image, audio, text) is converted into numerical vectors called embeddings that AI models can process, using specialized encoders like Vision Transformers for images and waveform encoders for audio.
• Projection: These vectors are converted into a shared dimensional space so the model can compare and reason across different data types, similar to converting currencies into a common unit.
• Joint Reasoning: All encoded inputs are processed together through the model's attention mechanism, allowing information from any modality to influence the reasoning about any other modality in a single pass.

The Nvidia collaboration is specifically designed to accelerate this process. Aive has integrated Nvidia's Nemotron models, which are open-source language models, into its own technology stack. According to Reynaud, this integration lets brands, broadcasters, and media companies make years' worth of existing video content accessible to AI assistants without having to recreate or manually rewrite it.

What's the Market Opportunity?

Aive raised 15 million euros last November to fund development and international expansion. The company unveiled its video GEO platform at VivaTech, and the response has been significant. Brands, television companies, and media organizations are now inquiring about making their video content discoverable by AI systems.

The broader multimodal AI market is growing rapidly. According to market research, the global multimodal AI market is projected to grow from 3.29 billion dollars in 2025 to 93.99 billion dollars by 2035, expanding at a compound annual growth rate of 39.81 percent. The multimodal segment commands the highest projected growth rate in generative AI, at 56.6 percent compound annual growth rate.

Reynaud believes that making video understandable to language models could become as important as optimizing websites once was for traditional search engine optimization. If that bet pays off, the next generation of AI assistants won't just retrieve videos; they'll understand them, extract knowledge from them, and cite them as sources in their responses.

The shift represents a maturation of AI capabilities. Rather than focusing on generating new content, the industry is increasingly recognizing that understanding and extracting value from existing content may be the more pressing frontier. For companies with vast video libraries, this technology could unlock years of content that currently remains invisible to AI systems.