How AI Vidia shipped 1,834 AI videos in 12 months (and what worked)
The production line that moved AI Vidia from concept to 1,834 shipped AI videos in 12 months. Inside: the pipeline, the monthly volume curve, and three things the AI Vidia team would do differently from day one.
The short version. AI Vidia, a performance creative studio, shipped 1,834 AI videos across 48 brand accounts in 12 months. The production line did the work, not the tool. Here is the pipeline, the monthly volume curve, and the three decisions the AI Vidia team would make earlier if it were starting again.
The production line view
1,834AI VIDEOS SHIPPED
48BRANDS
14COUNTRIES
99.2%BRAND-SAFE PASS
AI Vidia produced no single breakthrough output. The team produced a cadence. Brief goes in on Monday. Twelve variants ship by Thursday. Winners are rebriefed on Friday. Losers are logged and killed. That loop, not the model, carries the volume.
The production line view. Every video in the 1,834 number moved through brief, render, review, and ship in a repeatable order.
The monthly curve
Month one: 42 videos. Month three: 140. Month six: 210. Month twelve: 287. The slope changed every time the AI Vidia team collapsed a manual step. Brief templating. Pre-approved style locks. Auto-ratio export. Each removed a half day from the cycle.
Benchmark: AI pipeline vs traditional
Before the 1,834 number, the AI Vidia team ran the same brands through traditional agency production and in-house film teams. The gap is not marginal. It is a different unit economics regime.
Metric
Traditional agency
In-house film team
AI Vidia pipeline
Unit cost per variant
$3,200
$1,400
$520 (62% lower than in-house)
Time to first variant
18 days
9 days
48 hours
Variants per month per brand
4 to 6
10 to 15
40 (1,834 across 48 brands)
Iteration speed
1 round per week
2 rounds per week
3 to 5 rounds per week
Blended ROAS on winning variants
1.1x
1.6x
2.4x
Two reads on the table. First, the cost column is the easy story; the iteration-speed column is the compounding one. Second, the 2.4x ROAS is not a model story. It is a volume-times-learning story: more variants shipped per week means the winners surface faster and the losers get killed sooner.
Kevin's take
Design your pipeline around the variants you are willing to throw away, not the ones you hope will win. The throwaway rate is the real lever.
Kevin Dosanjh, founder, AI Vidia
A concrete example: in month three of the 1,834 run, we caught our own team polishing Reels variants that were already dead in the ad account by day three. We cut finishing time on anything below the 60 percent ROAS threshold. Weekly shipping volume jumped 40 percent without adding headcount, and the quality of winners went up because more review time went to the ones that mattered.
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Before a team builds the production pipeline below, it should pass the five-question readiness check. Teams that skip this step build throughput they cannot govern.
Step 1: brand lock in place. Can you point to a single document that defines lighting, framing, palette, plateware, and typography for the brand? If the answer is no, no pipeline throughput will save you. The first week goes to the lock, not the renders.
Step 2: throwaway tolerance. Are stakeholders aligned that at least 60 percent of variants will ship unpolished and die inside a week? If finance, legal, or brand treats every variant as a hero asset, the pipeline will choke at QC.
Step 3: ad-account access. Do you have direct access to the media buyer's ad account and performance data within 48 hours of shipping? Without it, the brief-to-rebrief loop stalls and the 2.4x ROAS number does not compound.
Step 4: single-owner stations. Is there one named owner for each of brief, render, QC, cut, and ship? Shared ownership across stations is the single biggest cause of pipelines that run at half throughput.
Step 5: kill criteria on paper. Have you written down the ROAS, CTR, or cost-per-result threshold below which a variant is killed without finishing? Teams without a kill threshold finish every variant and ship half the volume.
The AI Vidia AI video production pipeline
The 1,834 number is a throughput result. The pipeline below is the machine that produced it. Five stations, each with a single owner and a gate to the next.
Station 1: brief intake. Every brief lands in a shared template with hook, target surface, ratio cuts, and brand-safe flags. Brief lead tags the model (Sora, Veo, Runway) and the label category (exempt, required) before anything renders.
Station 2: render farm. Parallel render against the tagged model. First-pass budget is three renders per variant. Anything beyond three gets re-briefed, not re-rendered. This single rule killed 30 percent of wasted credits in month two.
Station 3: brand-safe QC. Every render goes through the 14 point brand-safe rubric. Hands, kerning, product accuracy, logo integrity, music licensing. 99.2 percent pass rate across 70,342 images and 1,834 videos comes from this station, not from the models.
Station 4: ratio cuts and captions. The winning render gets cut to 9:16, 1:1, and 4:5. Captions baked per market. Language pass per locale. Cut time averages under 20 minutes per asset on steady state.
Station 5: ship and log. Upload to Ads Manager with label toggle set per brief. Log the asset id, model, render count, and QC pass into the performance tracker. Next week's brief pulls from this log to pick winners to double down on.
What the AI Vidia team would change from day one
First, build the style lock before the first render. AI Vidia lost early weeks regenerating the same brand over and over because nothing was locked. Second, log every variant with its brief and its result in a single sheet. AI Vidia can now query that sheet and spot winning patterns in minutes. Third, treat the media buyer as a teammate, not a stakeholder. The brands that gave AI Vidia ad account access shipped twice as fast as the brands that did not.
The biggest operational change: kill disposable variants before finishing time, not after.
Frequently asked questions
01How many AI videos can an AI Vidia team ship in a month?
AI Vidia shipped an average of 152 AI videos per month across 48 brand accounts in the last 12 months. Peak months reached 287 videos. Volume depends on how many brands have a locked style system in place. Brands on a Performance Retainer average 40 on-brand AI videos per month.
02What is the biggest bottleneck in scaling AI video production?
The biggest bottleneck is not the model. It is the brief-to-asset loop. AI Vidia cut its cycle from 7 days to 48 hours by templating briefs and pre-approving style locks. The AI Vidia team tracks every variant against its brief in a single log so winners can be rebriefed the same week.
03What does AI Vidia mean by a style lock?
A style lock is a fixed combination of lighting, framing, palette, plateware, and typography that every AI Vidia render for that brand must match. The AI Vidia team builds the lock by rendering against the brand's existing hero imagery until the AI output is on-brand on the first pass. Style locks are the single biggest reason AI Vidia's first-pass approval rate stays above 90%.
Next step
Get your first 12 on-brand AI variants in 14 days.
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