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BUILD LOG

Conversion Render11 daysFigma + Blender → After Effects

Egentify's AI doesn't answer support tickets. It acts on them — changes addresses, moves orders, issues refunds. That is the product's strongest claim and its most frightening one, so the render is sequenced by objection instead of by feature.

BUILD METRICS

0Strategic revisions7 items returned, all execution and polish
11Days to delivery
6Screens, one datasetevery number cross-checks against every other
37sFinal runtime14 cuts · 2.64s average shot
0
Strategic revisions· 7 items returned, all execution and polish
11
Days to delivery
6
Screens, one dataset· every number cross-checks against every other
37s
Final runtime· 14 cuts · 2.64s average shot

THE PROBLEM

Egentify is AI customer support for e-commerce. The category is saturated with chatbot demos, and the one thing that separates Egentify — an agent that takes real actions inside a live store — is also the thing a store owner is most afraid of. The asset had to work cold: dropped into outreach, autoplay-muted, no prior context, no reason to watch past second two. A feature tour would have demonstrated the capability and triggered the fear in the same breath. One objective, one target viewer, one outcome, which meant every second had to be spent removing a reason to say no.

THE BUILD

The script was built as an objection ladder rather than a feature list. Eight questions a store owner asks, in the order they actually ask them — does it do anything, how is this different from a chatbot, will it act without my permission, what happens when it can't cope, does it move a number, how much setup does this cost me, what does a ticket cost, what's my downside — and one beat assigned to each. The storyboard shipped rearranged: the human-control beat had to land before the 91% containment stat. In the other order, the stat reads as the AI running your store unsupervised, which is the fear rather than the sell.

The load-bearing beat is two seconds long and shows the agent doing nothing. A customer asks to change a shipping address; the agent surfaces the change and asks “Ship order #4821 to this address?” with “No thanks” sitting next to it. Later, on a damaged-item ticket, the agent declines to issue the refund itself and escalates to a human, who approves $148.00. The capability gets demonstrated by being withheld. An agent that can act and asks first is a different purchase from one that merely can.

Six screens, one dataset. The Trail Jacket is priced $148.00 on the storefront, and the refund a human approves four beats later is $148.00. Ticket #2288 is opened 41 minutes ago; the dashboard reads “Urgent 2 · oldest 41 min”. The containment card's 1,391 of 1,529 chats is 90.98%, and the counter resolves on 91%. The demo data was built once and every screen reads from it — a product video that contradicts itself is a video the client can't put on their homepage.

Cut to the track, not to the animation. The score runs ~148 BPM with transients roughly every 0.41s and a 32-bar arc resolving at ~33s, so the end card sits on the resolve instead of after it. Fourteen cuts, 2.64s average shot length, every cut landing on peak motion rather than waiting for a move to finish — the eye leaves while the frame is still travelling. That is what holds a cold viewer for thirty-seven seconds.

THE RESULT

Delivered on Day 11. One revision round, seven items, every one of them execution and polish. No beat, no ordering decision, and no claim was rejected.

BUILD SPECS

sourceclient Figma + live product audit
outputMP4 (1920×1080, 30fps, 22 Mbps)
pipeline16bpc → ProRes 422 HQ → Premiere → H.264 2-pass
processobjection ladder → script gate → storyboard → animation
build11 days
vonone (music-led, 148 BPM, 32-bar arc)
revisions1 round, 7 items
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