Most media libraries hold far more footage than anyone can search, and the number of people who need to use that footage is only increasing. Marketing wants the right moment from a 50-year archive to promote a relaunch. Brand teams are trying to catch AI-generated promos that put the wrong characters on screen. And when former talent asks whether that's them in a new trailer, legal needs to trace every shot back to its source.

Ceivo, an AI-driven media asset management (MAM) platform, and TwelveLabs, the video understanding company, work together on exactly that problem: one searchable view of the library across cloud and on-prem storage, with governed access for people and AI agents alike.

This post is based on a webinar with Ceivo and TwelveLabs that included a live demo. If you'd rather watch than read, the on-demand recording is available now.

The Problem: More Users, More Risk

Enterprise AI adoption often started with the teams that own the content, such as localization and QC. It has since spread to nearly every department, speeding up creation for some teams and adding review work for others.

Take a brand with a 20-season animated series licensed to hundreds of partners worldwide. Each licensee can cut promos and social clips, and many now use generative tools to do it. The results can include characters from the wrong seasons mixed together, extra fingers, and faces that blend into each other, and someone on the brand side has to catch these errors before they go out.

The usual response is to buy a point solution for each team’s problem: one for localization, one for contracts, one for brand compliance, etc. Each one brings its own model, its own fine-tuning and retraining, and often its own cloud. That approach multiplies cost and governance burden, and often means handing each vendor your library to train on.

The Solution: Understanding Plus Governance

The alternative is to solve these problems once, at the library level, with one layer that understands the video and another that governs access to it. In the simplest sense, it’s like TwelveLabs is the engine and Ceivo is the car.

Video understanding comes from TwelveLabs. Marengo indexes the library, and Pegasus reasons about what's happening in a scene. Ceivo connects to libraries wherever they live (Amazon S3, other clouds, private storage, and on-prem systems through Signiant) and presents one view.

Governed access comes from Ceivo. Ceivo exposes the library through the Model Context Protocol (MCP), the open standard AI assistants and agents use to connect to enterprise systems. Each user or agent sees only the slice of the library they’ve been permitted to see. Beneath that governance layer, a knowledge layer tells AI assistants what’s in the library. When one connects, Ceivo gives it a summary of that slice, so the assistant spends fewer tokens exploring.

Every action a user or agent takes is logged in a chain of custody, which helps security teams feel comfortable opening the library to a wider audience. If a trailer made with a generative tool draws a “that looks like me” complaint from former talent, legal can ask Ceivo from Copilot which shots and source videos were used, without logging in to the asset management system themselves.

Ceivo Platform Overview

Those two layers are what let Ceivo serve the whole organization, not just the media operations team. Several capabilities put that foundation to work for different parts of the business.

Search: Teams can find footage by describing it in plain language or uploading a reference image, powered by Marengo’s indexes. A photo of a character, a Coca-Cola bottle, or “somebody in a yellow jacket standing in front of a tree at sunset” would all return matching shots, even if no one explicitly tagged it. Results land on a frame-accurate timeline, with markers for logos, black frames, transitions, and ad breaks, plus a scene list where Pegasus describes what’s happening in each one. Editors can run the same searches from inside Ceivo’s panel for Adobe Premiere without leaving the tool.

Brand Toolkit: The Brand Toolkit holds a library’s approved characters, products, and permitted contexts, and Ceivo can generate it with AI from existing documents. The toolkit also sets rules for which characters may appear together, so a promo doesn’t pair a season 1 character with one from season 20. Ceivo’s agents check content against the toolkit through MCP, using plain-language rules that teams can edit.

Findings appear right on the video timeline, including unapproved characters, potential MPAA violations, visible trademarks, and background music, and a compliance report summarizes them for review. Ceivo also uses its testing tool to configure these agents: in one test on a single clip, TwelveLabs’ Pegasus was one of only two models that reported the characters accurately, while the others invented a character who wasn’t there.

Partner Portal: Ceivo Atrium is a branded portal for licensees. When a partner submits new marketing content, such as a promo, social ad, or poster, Ceivo’s agents scan it for alterations, brand violations, and viruses. A poster with a Ford logo gets flagged unless Ford is in the show’s approved logo kit. The submitter sees what was flagged and where, and can either fix it or submit anyway. On the licensor’s end, a per-partner dashboard tracks each licensee’s failed checks, including AI-generated errors, so the brand can see how well each partner is following the licensing agreement.

Model Testing: Models are often priced in different units, which makes costs hard to compare, and the total depends on the content as well as the model. Before committing to one, teams can use Ceivo’s testing tool to estimate what it will cost, and how long it will take, to index their library with each one. Denser content means more scenes to process: a slow drama might need 4 or 5 shots per minute, while high-action sports needs 8 or 9. Indexing speed matters as well, particularly for post-production teams receiving new footage from set all day.

Key Takeaways

AI is now reaching into the media library from every department, and the teams responsible for it need more than a better search box. TwelveLabs understands the content, and Ceivo governs who and what can act on it. For teams planning their own rollout, four lessons stand out:

• Separate understanding from governance. One video understanding layer plus one governed access layer scales better than a stack of point solutions.

• Test models on your own content. Cost, token use, and accuracy vary by library, so your own archive is a better test of which model fits your use case than someone else’s benchmarks.

• Support the teams that review the content. As AI speeds up content creation, automated checks help review teams keep pace.

• Give agents roles, not blanket access. An agent can only do what its permissions allow, and a chain of custody logs everything it does.

Getting Started

Ceivo and TwelveLabs are offering an 8-week pilot. In week one, the team stands up Ceivo and TwelveLabs and ingests your library. Over the next three weeks, they’ll build three agents for your use case at no charge. Learn more at ceivo.com/lfg or reach out to the TwelveLabs team directly.