Three teams run three routines. A livestream team airs two hours every week and needs five or six clips by morning; a producer watches the whole replay after the show to mark the moments. An education team records a three-hour session and needs eight focused lessons, each captioned. A product team posts a talking-head update weekly and spends an hour per episode transcribing and timing captions that no one on the team has.
All three jobs share one shape: the raw footage already contains every frame of the result, and the work is deciding what stays, in what order, with what words on screen. None of it needs new pixels invented. What the teams want is an AI that performs the edit — and a handoff point where a person can still fix the machine’s calls. That distinction, AI that makes the edit versus AI that generates imagery, decides which SDK fits these jobs.
What Meishe’s SDK Gives You
The SDK lines its AI products up against exactly these jobs, inside one engine.
AI smart editing does the first pass. It is the AI that scans the footage, finds the usable parts, trims the dead air, and orders the clips — you approve or adjust. On the livestream replay, speech detection maps who talked and where the energy rose, and the AI turns that map into a plan: these segments, in this order. The producer’s two-hour watch becomes a minutes-long review.
AI captions covers the words. Captions are generated from the speech, landing on the timeline where you can fix any word. The lesson recordings get their captions as one step inside the engine, and the talking-head’s proper nouns get corrected once, on their own track, without disturbing the edit.
AI video effects and AI beauty clean the picture. AI video effects are filters, transitions, stickers, and AR looks — installable packages applied as timeline elements, so a one-tap look can cover a cluttered background. AI beauty is face touch-up on sliders users already know from photo apps; uneven light on a face evens out in one pass.
AI Vlog rolls the same machinery into a whole day’s footage: point the camera or drop in your clips and it turns into a vlog-style video — highlights picked, cuts made, music and captions on. Every decision stays open in the editor afterward.
The editor underneath. The multi-track editor has drag-and-drop trimming, keyframes, and styling restyleable to your brand. Its engine core is C++, running in the browser through WebAssembly and natively on iOS, Android, and desktop — scrubbing a three-hour recording never waits for a server. Background removal ships alongside, cutting the person out of the background as a switch on the clip. A finished plan is a JSON project, and the same project renders to the identical video every time.
Meishe’s Advantages
The first advantage is the handoff, and it comes from both creation modes living on one platform. The interactive editor and the automated pipeline share one engine and the same project files. The AI’s draft is not a baked file; it opens in the editor as a real project. A producer trims where the cut ran late, drops one clip, promotes another — minutes instead of the two-hour watch. Render the same project twice and the result is identical, frame for frame.
The second is private deployment. The whole platform runs on your own servers, on-premise or in your cloud. Course recordings, livestream replays, and user footage never pass through anyone else’s machines. For an education platform or any team whose content policy says stays-in-house, that settles the question before the shortlist does.
The third is the AI’s place: inside the engine rather than wired around it. Every result lands as something on the timeline — a text track, an effect element, a trim — not as a finished file a person must accept or re-roll. There is no transcription service to wire in, no AI vendor to evaluate, no files to re-import. That is what makes the machine’s work checkable, which is the whole point when the machine is making the edit.
How Cooperation Works
The path starts with the editor demo, free to try. A team opens it in a browser and puts its own footage on the timeline — scrub, trim, caption — to see the checks in the handoff for real. Next comes a technical discussion with Meishe’s engineers on fitting the SDK to the product’s platforms and jobs, from livestream replays to lesson slicing. The commercial licensing discussion follows, scoped to what the product ships. The last step is deployment on the team’s own infrastructure, on-premise or in its cloud. Each step answers a concrete question: the demo covers the handoff, the discussion covers the fit, the license covers the deployment.
Common Questions
What does “AI makes the edit” mean versus AI inventing new pixels? Making the edit means planning cuts, ordering clips, and writing captions, with every frame of the result already in the footage. Generated imagery invents pixels, and unpredictability is the wrong ingredient for a final assembly. Meishe’s AI products are the first kind.
Which platforms does the SDK cover? The engine runs in the browser through WebAssembly and natively on iOS, Android, and desktop, and renders on servers. One project format covers every surface.
Can the editor carry our own branding? Yes. The editor is restyleable to the product’s own look. Users of a course app see the course app’s editor.
Do the AI features need outside services? No. AI smart editing, AI captions, AI Vlog, and the rest ship inside the SDK. There is no transcription service to wire in and no files to re-import.
Can automated and manual editing coexist? Yes. The AI’s plan is a project file the editor opens directly. The machine takes the first pass; the person takes the fixes; nothing converts in between.
How do we evaluate it for our product? Point AI smart editing at one real replay or lesson recording and open the draft in the editor. The review pass either takes minutes or it does not.
The teams that want AI editing their footage all want the same contract: the machine plans, the person checks, and the result is deterministic. A platform where the AI works inside the engine and hands over a real project — running on infrastructure the team owns — is the shape that delivers it. The rest is preference.