AI assistants help most in the text-heavy parts of video production: drafting scripts and shot lists, turning notes into briefs, planning cutdowns, sorting through feedback. They're weak wherever the work depends on seeing footage, because a general assistant can't watch your video files unless you give it a way in. For an in-house creative team, a practical split is to let the assistant write around the footage, and to connect it to a search tool once it needs to know what you've already shot.
What an assistant does well out of the box
Before a shoot, an assistant can turn a campaign brief into a script broken down by scene, a shot list with coverage notes, and a first draft of the call sheet. After the shoot, it can turn the editor's notes into a revision list, draft captions and on-screen text for each cutdown, and go through several rounds of stakeholder feedback to find what's still open. None of that needs the assistant to see a frame. It needs clear inputs and a person who checks what comes out.
It falls short on anything that starts with "we already have a shot of...". The assistant has no idea what's in your ad archive, and it will happily write a shot list that asks the crew to reshoot something sitting in last quarter's footage.
Four ways to let an assistant see your footage
Paste it in. You can attach frames or screenshots to a conversation and ask about them. That's fine for checking a single shot. It doesn't scale to an archive, and nothing carries over, so next week you paste again.
A filesystem connector. Some MCP servers let an assistant read a folder. The assistant then sees file names, dates and folder structure. It still can't see what's in the picture or hear what's said, so "find the unboxing shot" only works if someone named the file "unboxing".
Build your own index. Transcribe everything, pull frames, embed both, store them in a vector database, and expose a search tool to the assistant. This is the most flexible route and the most work, and the embedding step alone brings its own cost and upkeep. Without an engineer assigned to it, this kind of project tends to stall after the prototype.
A hosted video search connector. You upload footage to a service that indexes it and offers search over MCP, so the assistant can call it in the middle of a conversation. In exchange, the footage has to live in that service, and searches count against a usage allowance.
Check existing ad footage from an AI chat before the next shoot
Vivu connects to any assistant that supports MCP, such as Claude or ChatGPT. You ask the assistant to open the project's secure upload page, upload last season's ads and product videos in the browser, and the footage is indexed once in the cloud. Then, with the shot list open in the same chat, ask something descriptive, like "find the moments where a price breakdown appears on screen" or "find where the product first shows up in someone's hand". A precise search runs as a job you wait on, then returns time ranges you can open, each with a one-line reason, and you can preview them one at a time on the results page.
The assistant can then mark which shot-list lines are already covered by existing footage. For those, choosing Export original clip on the results page gets the source file ready for your editor. The reason line often paraphrases on-screen text, so check any number against the clip before it goes into a script.
When this route doesn't fit
Each Vivu search runs inside one project, and footage has to be uploaded and indexed in the cloud before the assistant can find anything in it. Searches draw on an allowance (there's a free tier and paid tiers, with limits on Vivu's pricing page), and results are time ranges you open, not frame-exact timestamps. If you want the assistant to cut the ad or produce variants, you need an editing or generation tool, because this route only finds what you already shot. It also won't tell you which ad performed better. That data lives in your ad platform's reporting, not in the footage.
You may not need any of this. If your team shoots fresh for every campaign and rarely reuses anything, the assistant's writing work covers most of what it can do for you. Chat-driven editing tools are a separate category, for when the goal is changing the cut rather than finding material.
Deciding which way to go
Count how often a shot list in the last few months asked for something your team had already filmed. If that almost never happens, keep the assistant on scripts and briefs. If it happens every campaign, connecting the assistant to your footage will cost you less than another reshoot, and drafting briefs from footage you already have becomes a normal step rather than an afterthought.
FAQ
Can an AI assistant watch a folder of my video files?
No. You can attach frames or screenshots to a conversation and ask about them, but an assistant can't open a folder of footage and know what's in each file. It needs a connector to a service that has already indexed the video, or a search index you build yourself.
Will an AI assistant tell me which ad creative works best?
Not from the footage. Performance data sits in your ad platform's reporting. An assistant can summarize a report you give it, but a video search tool only knows what appears on screen and what's said.
Do we need an engineer to connect an assistant to our footage?
Not for a hosted connector. Adding a remote MCP address and signing in happens in the assistant's settings. Building your own index with transcription, embeddings and a vector database does take engineering time, both to build it and to keep it running.