Input: one mobile recording and one task result
Output: a 30-second app explainer
Aspect: 9:16 and 1:1
Build log
1. Set the rule
I opened the screen recordings, feature notes, and one target customer, locked the real screen recording and buyer workflow, and wrote down what could not change.
2. Run one minimum version
I made one small a 30-second app explainer first. The app UI was too small on mobile, so I cropped to the active panel.
3. Build the focused variant
I reused the same reference and changed only one screen recording and one short outcome line.
4. Review and export
I checked cursor visibility, UI readability, and one CTA at full size, then exported the final 16:9 and 1:1.
Complete execution record
Task boundary and source gate
Treat AI app explainer video as a deliverable, not a definition. The job receives one mobile recording and one task result and owes a 30-second app explainer in 9:16 and 1:1. Write the rejection reason first, then turn it into the first hard constraint.
Require one buyer task, clean demo data, permission to show the interface, the result metric, and a privacy review. Mask customer names before anything is generated. Do not invent integrations, uptime, savings, or customer data. The demo should prove only what the current build can do.
The imgmov route
Use the real screen recording as the anchor. Canvas places problem, click, result, and CTA; generation only fills connective motion. Interface text, cursor state, and result field stay true to the recording.
Keep platform requirements outside the model prompt. The prompt can describe a scene; a named checklist records the crop, claim, consent, and console check that make it deliverable.
Lock the variables that cannot drift
Lock UI text, button labels, workspace name, cursor path, error state, result value, logo position, and claim wording. A demo that shows the wrong field is worse than no demo.
Put the locked fields at the top of the prompt, not at the end: The practical move is to lock the real screen recording and buyer workflow and change only one screen recording and one short outcome line. Save that prompt with the reference, ratio, and model so the next run starts from a decision instead of a guess.
Step-by-step build path
1) Set the rule: I opened the screen recordings, feature notes, and one target customer, locked the real screen recording and buyer workflow, and wrote down what could not change. Leave one checkable artifact from this step; do not start the next until it exists. 2) Run one minimum version: I made one small a 30-second app explainer first. The app UI was too small on mobile, so I cropped to the active panel. Leave one checkable artifact from this step; do not start the next until it exists. 3) Build the focused variant: I reused the same reference and changed only one screen recording and one short outcome line. Leave one checkable artifact from this step; do not start the next until it exists. 4) Review and export: I checked cursor visibility, UI readability, and one CTA at full size, then exported the final 16:9 and 1:1. Leave one checkable artifact from this step; do not start the next until it exists.
Change one named variable per rerun: prompt, reference, camera, duration, model, ratio, or export crop. If two variables change together, a better output cannot be reused because nobody knows which fix worked.
Review gates and evidence
At full size, a buyer should read the button, follow one path, and see the source of the result without marketing language covering the proof.
Keep source, approved wording, rejected version, correction, credit cost, and final crop next to the asset. The record exists so the next reviewer can reproduce the decision without a chat thread.
Failure diagnosis and retry ladder
If UI warps, keep the real frame and animate around it. If the path branches, split the demo. If the metric is unclear, show the source field in your external editor.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
Versioning and handoff
Deliver sales, docs, and social cuts separately: sales leads with pain, docs leads with task, and social leads with the visible result.
The folder is boring on purpose: source, approved reference, generation settings, caption file, platform cuts, QA screenshots, and one correction line. A reusable asset is boring in the right way.
Why this is not a generic answer
The imgmov advantage is the chain: Asset Library preserves the subject reference, Canvas fixes shot order and first/last frames, the workspace routes Agnes to Seedance, Kling, or Veo only after proof, and your external editor adds captions and CTA without regenerating the media.
Do not invent integrations, uptime, savings, or customer data. The demo should prove only what the current build can do. If the request only asks what ai app explainer video means, a search page is faster. This page is useful when someone must deliver a 30-second app explainer under real constraints.
Frequently asked questions
What is the one rule I keep repeating?
The practical move is to lock the real screen recording and buyer workflow and change only one screen recording and one short outcome line.
The workflow is specific about its stopping point: it starts with one mobile recording and one task result and stops at a 30-second app explainer. In imgmov, upload the source, lock it as a reference, set 9:16 and 1:1, run one proof, and save the passing settings. If a reviewer cannot tell which version is approved, the workflow has failed even when the media looks good.
What do I check before export?
Actual UI, cursor, active panel, pacing, and final crop.
Do not upgrade on hope. A 5-second Agnes Video probe is 10 credits. If the concept passes, Seedance 2.5 costs 90 credits at 480p or 195 at 720p for 5 seconds; Kling is 195 without audio or 245 with audio. Veo enters only when a 4/6/8-second cinematic bucket is genuinely worth 80/125/165 credits. Prove hook, subject, and rhythm first, then pay to clean up motion that already worked.
What makes this different from a generic generator?
I start from one mobile recording and one task result and keep a 30-second app explainer consistent, instead of inventing a new scene each run.
The check is not “does it look AI-nice?” It is: At full size, a buyer should read the button, follow one path, and see the source of the result without marketing language covering the proof. Then the source, approved copy, rejected version, correction, and final crop stay in the same handoff folder.
What should I do if the first render drifts?
Cut the motion, lock the reference, and rerun one small version before batching.
If UI warps, keep the real frame and animate around it. If the path branches, split the demo. If the metric is unclear, show the source field in your external editor. Change one named variable, keep the old version, and record the credit cost. If the same failure repeats, fix the reference or scope rather than asking the prompt for forgiveness.