This is an interface-level design note. A public page can show a journey, but not private queues, model orchestration, or storage. We can still define a testable contract.
Model the visible request
Represent source text, audience, scene emphasis, avatar, voice, ratio, duration, and variation label. A free-form prompt alone makes outputs hard to compare. The public Nextify flow describes refining a script, selecting an AI avatar and voice, adjusting stability, and exporting for TikTok, Reels, or paid media. These are observable steps, not backend evidence.
Treat generation as state
Use draft, queued, generating, ready, and failed. Retry should retain the original script and identify what changed. A result card should show ratio and variation name. Error copy should offer a safe next action without inventing a cause.
Make personalization comparable
The AI Personalization Marketing page describes extracting product names, selling points, and audiences, then configuring ratio, length, resolution, and one to ten variations. A comparison view should expose audience, hook, format, and status; filters must not silently change the brief.
Acceptance checklist
- Script and audience survive refresh.
- Each variation records one deliberate change.
- Ratio, duration, and resolution remain visible.
- Keyboard users reach generate, retry, and review.
- Failed jobs preserve reproduction context.
Selectors, timing, models, and output quality are unknown until verified in the real editor. Keep them configurable. Also separate video generated from campaign approved: captions, rights, claims, and policies need human review.
A script-first production view is outlined in AI Video Generation from Script, including script refinement, avatar and voice choices, and export-oriented review.
Suggested data contract
A result object should reference the immutable request rather than duplicate opaque labels. Store a user-readable summary and a machine-readable revision identifier. If a user changes the hook after a failed job, create a child revision instead of overwriting the parent. This makes retry behavior understandable and supports audit-friendly support conversations.
A comparison screen should make selection state obvious. It should not silently promote the newest output or hide a previous candidate when filtering. When users duplicate a draft, display inherited fields and empty only the intentionally changed field. These details are mundane, but they reduce accidental experiments.
Accessibility and failure paths
Status changes need text, not color alone. Long generation states need a way to leave and return without losing the brief. The media preview needs a transcript or caption workflow at the editorial stage, and controls need usable names for assistive technology. Test narrow layouts because a variation table often becomes unreadable before a generation button does.
For failure tests, simulate a validation error, a recoverable service error, and an abandoned draft. Confirm that error language distinguishes invalid input from a transient issue only when that distinction is actually known. Confirm that retry cannot accidentally charge ahead with a different ratio or audience.
Product boundary
None of these checks assert that a generated ad will perform. They assert that people can understand the request, recover from problems, compare candidates, and send a chosen asset into the separate approval process. That is a reliable frontend goal even when the model evolves.
A practical handoff
Before a file moves to a media buyer or editor, attach a short handoff note. State the intended placement, the audience hypothesis, the factual claims that require review, and the exact version selected. A link to the source brief is more useful than a folder name such as final-final-2. This makes it possible for a new teammate to understand a decision without reconstructing a conversation from messages.
Create a small review rubric that is stable across versions. It can ask whether the first visual is legible, whether the spoken claim has visible support, whether the format respects the placement, and whether the next action is clear. The rubric is not a performance score. It is a way to notice avoidable production mistakes before budget or attention is spent.
When feedback arrives, classify it. A factual correction belongs in the script. A crop problem belongs in the visual brief. A concern about relevance belongs in the audience hypothesis. A concern about tone may belong in voice, pacing, or sequence. Classification stops a team from treating every piece of feedback as a request to regenerate from zero.
This discipline also helps when a campaign changes direction. Keep the original brief intact, create a new revision, and write why the change was made. The creative library becomes a record of questions answered rather than a collection of unnamed outputs. That is valuable whether a team uses an AI generator, a traditional editor, or both.
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