In a world where video dominates both our personal and professional lives — U.S. adults spent an average of six hours and 45 minutes watching video last year alone1 — the challenge of managing video content is becoming increasingly urgent. Industries like law enforcement and media production face the daunting task of sifting through thousands of hours of footage, often relying on outdated methods like manual tagging or basic keyword search. Veritone’s multimodal AI video search promises to transform this process, enabling organizations to more efficiently find objects and scenes in a rapidly expanding haystack of unstructured video data.
Video typically has no built-in labels or keywords to facilitate search, and indexing footage based on time stamps, date, device identification numbers or even optical character recognition will only get you so far. Fortunately, recent advances in AI are accelerating video search’s evolution. One of the most promising avenues of research involves the use of AI models that can simultaneously analyze visual elements, audio components such as speech or soundtracks, and textual information, including subtitles and speech transcriptions.
Watch below as Markus Toman, Principal Applied Scientist and Head of Labs at Veritone, explains:
Veritone introduced just such a multimodal AI approach earlier this year at the 2025 National Association of Broadcasters (NAB) conference as a new feature within our Digital Media Hub (DMH). Veritone’s multimodal AI video search demonstrated how DMH works with complex, natural-language commands like “show me instances of brand logos in last week’s footage” and instantly surfaced highly accurate results — even across poorly labeled archives.
Find both the forest and the trees
Multimodal video search is a big shift in how we search and extract information from multimedia content. For example, searching for “trees” in a database might only return results explicitly labeled as “trees.” However, that same database may also label some multimedia content as “oak” without being able to correlate it to the term “tree.”
Here’s how multimodal video search changes this:
- No more manual tagging: AI handles this automatically.
- Broader accessibility: Removes the need for advanced technical skills, allowing users to search using plain language.
- Unparalleled precision: By analyzing different types of data together, relevant details are less likely to slip through the cracks.
Unlike traditional video search, multimodal video search uses multiple modalities to optimize its capabilities. “Multimodal” is the ability to analyze and combine different data types, compressing this information into compact representations to capture semantic meaning across different types of data.
What’s even more interesting about multimodal AI is its ability to recognize concepts through contextual understanding. For example, if you search for “Christmas celebration,” it could surface footage based on audio (bells ringing), visuals (holiday lights), or text (“Merry Christmas”), even if the footage isn’t explicitly labeled that way.
In addition to using multiple modalities and contextual understanding, Veritone’s multimodal AI video search also utilizes large language models (LLMs) to interpret user queries and map them to correct multimedia insights. This awareness enables flexible, intuitive searches without preexisting knowledge.
Balancing innovation with real-world challenges in video search
Traditional video search requires exhaustive preparation. Even with technologies like optical character recognition (OCR) or speech-to-text transcription, the process is error-prone and rigid. Typically, this can only output labels for a specific set of classes, which might not be what the user is interested in. For example, modern embedding models can be trained on millions of image–caption pairs to learn a vast range of concepts without needing to define every possible category in advance. Veritone’s multimodal AI greatly reduces the dependency on manual labor, offering:
- Automated indexing: DMH pre-processes video data into embedding vectors, enabling semantic search without exhaustive tagging.
- Faster results: Once indexed, searches are nearly instantaneous regardless of the database size.
- Flexibility: It enables searches by concepts to include logos, emotional moments or specific words.
The real power of Veritone’s DMH lies in its versatility as it improves workflows across both public and private sectors. Bodycams, dashcams, CCTV and drone footage create petabytes of unstructured video that law enforcement must store and access in accordance with data retention laws.2 With multimodal AI video search, they can search for specific scenes, combine data sources for detailed summaries and identify patterns to improve overall public safety. Likewise, media and sports organizations will be able to leverage multimodal AI video search to more easily generate highlight reels and create personalized, localized or thematic content based on their audiences. All of this helps to streamline workflows, reducing production timelines and accelerating video archive monetization.
However, it’s important to recognize the challenges that come with deploying multimodal AI. Processing large-scale multimodal data requires significant computational resources, which can result in high compute costs. Additionally, multimodal AI systems may occasionally produce hallucinated outputs—i.e., insights or results that are plausible but factually incorrect. Balancing the complexity of the technology with user-friendly experiences also remains a key focus for developers as the technology matures.
Redefining Video Search with Multimodal AI
The future of video search is as exciting as it is transformative. Veritone is exploring advancements in many areas, including LLM-powered tagging, semantic search, real-time auto summarization and dynamic workflows.
Listen to Veritone CTO Al Brown explain the magic of Veritone’s multimodal search capabilities:
Veritone’s approach allows clients to bring their own models or leverage the latest from the open-source community, ensuring true flexibility. Its DMH is more than just a powerful tool, as it takes video search to a whole new level. By combining audio, visual and text data into a seamless experience, DMH unlocks new efficiencies, deeper insights and exciting creative possibilities. As this technology evolves, it promises to put control back into the hands of content owners and users.
1https://content-na1.emarketer.com/digital-media-makes-up-nearly-two-thirds-of-consumers-total-time-spent-with-media
2https://www.propublica.org/article/police-body-cameras-video-ai-law-enforcement
