Four cinematic product spots, built to test a pipeline.

01 Why this exists

A creative pipeline is only real once it has produced finished work under the same constraints a paying job would impose: a fixed runtime, a defined format, and a bar that has to be cleared before anything ships. This run existed to establish that bar.

These are concept pieces. No brand commissioned them, none is identified in the published work, and they are not presented as client results. They are shown as evidence of production capability, which is the only claim they can honestly support.

02 Objective

Produce four distinct formats — a hyper-motion product ad, a cinematic spot, a creator-style piece, and an explainer — so the pipeline was tested across genuinely different demands rather than four variations of one look.

03 Production pipeline

The sequence: lock the assets, storyboard to a fixed frame count, generate, then harvest. Asset-lock comes first and matters most — establishing a consistent product reference before any motion work begins is what prevents the subject drifting between shots, which is the most common way AI-generated product video gives itself away.

Storyboarding to a fixed number of frames keeps a 15-second runtime an actual constraint rather than an aspiration, and makes the generation step a series of specific requests instead of an open-ended prompt.

04 Human and AI roles

AI generated the motion and imagery. Every creative decision that determined whether a piece was good — concept, shot order, pacing, what to cut — was human. Selection in particular is not automatable: the pipeline produces more material than ships, and judgement about which take clears the bar is the entire value.

A fifth spot was produced and cut. It read as visibly cheaper than the others, and shipping it would have lowered the average. The willingness to discard completed work is part of the process, not a failure of it.

05 What the run taught

Format is not a post-production concern. A vertical creator-style piece composed as though it were widescreen does not survive being placed in a widescreen context — it needs its own treatment rather than bars, and that has to be decided before generation, not after.

Volume is cheap and selection is expensive. The cost structure of AI production inverts the traditional one: generating alternatives is nearly free, so the scarce resource becomes the attention required to judge them. A pipeline that does not budget for that judgement produces a lot of mediocre output quickly.

06 Limitations

No performance data is presented here because none exists — these pieces were not run as paid media, so there is nothing honest to report about click-through, conversion or spend. Claims about how AI creative performs belong in work where it actually ran, and that measurement is in progress on Quantivo's own products.

07 Rights & disclosure

Produced from publicly available product imagery for demonstration purposes. No brand is identified in the published pieces, no client relationship is implied, and the motion is disclosed as AI-generated. Commissioned work follows Quantivo's AI content disclosure policy, including written consent for any synthetic likeness or voice.