From blank page to finished clip: my 4-step video workflow in one browser tab

Step-by-step, with the exact order I follow and where each stage usually breaks.

Last month I needed to ship a 45-second product clip for a side project. My old routine meant jumping between a script doc, an image generator, a video editor, a TTS tool, and a music library — five tabs, five exports, five rounds of re-uploading. This time I ran the whole thing inside one workspace, and the difference in speed forced me to write the process down.

Here is the exact workflow, step by step, so you can copy it.

Step 1: Start from the message, not the footage

Before touching any generator, I write the one sentence the video must land: "Five subscriptions replaced by one canvas." Every asset in the next steps gets checked against that sentence. If it doesn't serve the message, it doesn't get generated.

This sounds obvious. It is also the step everybody skips, and it is why most AI-generated videos feel like a demo reel instead of a story. Ninety seconds of scripting saves an hour of regenerating shots that don't fit.

Step 2: Block out the visuals as images first

Instead of prompting video immediately, I generate still frames for each beat of the script first — roughly one image per 6 seconds of final runtime. Working with an AI image generator at this stage is cheaper and faster than iterating on video: a wrong composition costs one regeneration, not a forty-second render.

Once the frames tell a coherent story in sequence, I know the video prompts are ready. The images become the storyboard, and the strongest two or three become thumbnails.

Step 3: Generate the motion where it matters

Not every beat needs motion. My rule: animate the moments with actual change (before/after, assembly, camera reveal) and hold the rest as static frames with light motion. A 45-second clip usually needs only four or five real video segments.

Text-to-video from the approved frames keeps the style consistent, because every segment inherits the visual language the storyboard already locked in. This is where the one-canvas approach pays off — the video opens directly beside the stills it came from, so drift gets caught immediately.

Step 4: Voice and music last, then assemble

Voiceover and music come after picture lock, never before. Recording the VO against the final cut means the pacing matches what's on screen, instead of cutting footage to fit a narration that was recorded blind. Background music sits under it at roughly 20% of the VO level — present, never competing.

Assembly happens in the same canvas: sequence the segments, drop the audio in, and export. No exports between tools, no re-uploads, no version-naming hell across five services.

Where this workflow breaks

Honest failure modes, so you know what to watch for:

• Skipping Step 1 produces pretty clips with no message. The fix is always the sentence test.
• Animating everything multiplies render time for zero payoff. Motion is a highlighter, not the text.
• Audio-first planning locks pacing before the visuals exist, and one slow generation then breaks the whole timeline.

The takeaway

The workflow itself is nothing exotic: message → storyboard → motion → audio → assembly. What changed for me was running it in Arui.AI — one browser tab where the image tools, video generators, voice, and canvas sit next to each other, with daily credits to test each stage before committing. Fewer handoffs, fewer exports, and a single place where every version of the project lives.

Start with the sentence. The rest of the pipeline falls out from there.