# Recommendations for AI video DAM or MAM software, sorted by what's broken > The right recommendation depends on what is actually going wrong. Canonical URL: https://vivu.ai/guide/do-you-have-recommendations-for-ai-video-dam-or The right recommendation depends on what is actually going wrong. If your video has no single home, permissions are a mess, and finals get lost between versions, you need a system of record. Get a DAM if the work is mostly finished brand assets, or a MAM if it is mostly raw production footage handed between editors. If the footage already lives somewhere you're happy with and the real complaint is that nobody can find the shot inside it, a new asset manager may not fix that. A search layer next to the storage you keep often fits better. ## When a DAM is the right call A DAM is built around finished assets: approved cuts, logos, product shots, campaign videos, and the rights and expiry dates attached to them. Bynder and Brandfolder are examples. Recommend one when people keep asking "which version is the approved one" or "are we allowed to use this", or when a company-wide video library needs controlled access for staff and interns who didn't make the footage. The cost is the setup and the discipline: someone has to upload, describe and approve assets, or the DAM becomes one more place where things go missing. If the difference between the two kinds is still unclear, [DAM versus MAM](https://vivu.ai/guide/what-is-the-difference-between-a-dam-and-a-mam) goes through it. ## When a MAM is the right call A MAM is built around production. It handles raw footage, proxies for remote editing, and the handoff between whoever shoots and whoever cuts. Iconik and CatDV are examples. Recommend one when the bottleneck is moving footage to editors and keeping track of what each one has, for example a small team that has more footage than it can edit and is starting to bring in remote editors. The cost is migration and process. Footage has to move in, editors have to adopt it, and someone owns the metadata. ## What "AI" usually adds In both categories, the AI features fall into two groups. The first writes metadata when footage arrives: tags, transcripts, detected objects. That makes a library searchable by the words in those fields, and it never makes the library searchable by a question nobody predicted when the tags were written. The second answers questions at search time against an index of what was said and shown. Ask any vendor which of these they mean. A library of thousands of clips described with metadata is searchable only as far as the metadata goes, and in practice metadata is the first thing that gets skipped once the library is busy. ## Keeping your storage and adding search Plenty of teams are happy with where their media lives. Their storage works well for remote editors and they have no wish to switch. What they lack is a way to ask "do we have a shot of X" without opening folders. The route here is a separate search layer. You upload copies of the footage you reuse, it gets indexed once, and people search it in plain language. The trade-off is that you now have a second place footage lives, new footage has to be uploaded too, and searches run within one collection at a time. That is the route [Vivu](https://vivu.ai/solutions/marketing) takes: the webinars, customer interviews and event recordings you reuse go into a project, and someone writing a brief can ask for "a customer explaining why they switched" and get back time ranges they can open, or check the project before booking a reshoot. Editing, approvals and the master files stay in the tools you already have. Checking existing footage this way before a shoot is covered in [checking for existing footage before reshooting](https://vivu.ai/guide/how-to-check-if-we-already-have-footage-before). ## When you need neither If the library is small, one person knows where everything is, and finals live in a shared drive with consistent names, a DAM or MAM adds process without solving a problem. The same holds if you rarely reuse footage. These systems pay off when footage is reused across projects by people who didn't shoot it. ## How to decide Write down the last three times someone couldn't find something. If the answer was a file, a version or a permission, buy the system of record that matches your work. If the answer was a moment inside footage you already had, add search before you migrate anything. ## FAQ ### Do we need both a DAM and a MAM? Most marketing teams need one, not both. If your video work is mostly finished assets shared across the company, a DAM is enough. If you shoot and edit a lot in-house with several editors, a MAM is enough. Teams with large production and large brand libraries sometimes run both, with finals published from the MAM into the DAM. ### Will a DAM help us find a specific moment inside a long video? Usually only as far as its metadata or transcript reaches. A DAM finds the file. Finding the minute where something happens means searching what is said and shown inside the video, which is a separate capability you should test directly. ### Is it worth migrating all our footage into a new system just to get AI search? Usually not, if search is the only problem. Migration costs time, retraining and broken links. Adding a search layer for the footage you actually reuse fixes the finding problem and leaves your storage alone. ### How do we get editors and non-editors using the same video library? Give them the same place to search and a different place to work. Editors need original files and proxies. Marketers and writers need to find the moment and hand it on. A library where non-editors can search by description, and editors can pull the original clip, serves both.