Case studies · 3 users
How AVA users catch AI video failures before they happen.
Anonymous case studies of real AVA users — solo creators, boutique agencies, brand accounts. Each story is structured the same way: customer profile, problem, before AVA, with AVA, workflow changes that stuck.
How a 200-clip/month Instagram brand recovered $387 in stranded Sora 2 credits
Instagram brand account using AI video heavily for product reels. Sora 2 was their stylized-motion workhorse — when OpenAI killed it in early May 2026, they had ~$387 in unused credits and a workflow built around Sora's specific aesthetic. …
Boutique production agency: $612/mo recovered + 18% retry rate cut after AVA + Runway Gen-4 migration
Production agency running AI video as a billable line item for boutique clients. Retry rate hovered around 30% across providers — translating to ~$420/mo of pure waste before any margin calculation. The agency tried to absorb this in client…
Solo TikTok creator: $84/mo recovered on Luma color drift, ROI in week 1
Solo creator working with branded products in the fashion/lifestyle space. Luma Ray-2 was their daily driver for stylized motion shots. But branded product shots kept failing on color drift — a blue dress would appear cobalt at frame 1 and …
More resources for picking the right tool before you commit credits.
Free calculator
Your real cost per usable clip
List × (1/first-try success) × (1+denial). No signup.
Alternatives guides
Ranked substitutes for every major tool
8 tools covered. Pick by shot type.
Head-to-head comparisons
Detailed pairwise comparisons
Runway vs Luma, Sora vs Veo, Kling vs Runway.
Failure reference
105 documented failure modes
Across 8 providers, with technical names.
Why anonymous
On the case-study format.
Most AI video customers do not want to publicly disclose their workflow changes — partly because it advertises their failure rate, partly because they keep their workflow as competitive advantage. We respect that.
All case studies are based on real AVA users with permission to publish anonymized. Customer types, monthly spend ranges, and pattern data are accurate. Specific provider names, failure categories, and workflow patterns are real — only the customer identity is obscured.
If you are an AVA user willing to be named in a case study, email joe@aivideoauditor.com. Named case studies typically include a 10–15 minute interview and your sign-off on the draft before publish.