sales_ops · media · workflow

Delphi builds premium client-facing assets in minutes with Mutiny

Delphi's go-to-market team needed to deliver personalized, premium client materials but creating them was slow, manual, and repetitive — custom landing pages required hours in Figma and Framer, generic documents undersold the premium product, and a whole tier of client assets never got made due to the execution bottleneck.

How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Brand identity extraction
Mutiny automatically extracts brand identity including fonts, colors, logo, and visual style from the company website.
Tools used
MutinyNotion · partnerFigmaFramerGammaClaude Cowork
Outcome

What used to take two to three hours in Figma now takes roughly three minutes in Mutiny, saving the team up to 20 hours a week and unlocking a new category of client assets the team could not previously justify creating.

What failed first

AI tools like Gamma and Claude Cowork evaluated before Mutiny could reach only roughly 75 percent of the desired quality, and getting the rest required so many iterations and manual corrections that the time savings evaporated.

Results
Time saved20+
Volumetwo to three hours in Figma now takes roughly three minutes
Cost replaced$0
Source

https://www.mutinyhq.com/case-studies/delphi-builds-premium-client-facing-assets-in-minutes-with-mutiny

How we source this →

Grounding & classification
Source type: vendor customer story
30 fields verified against source quotes.
content generationdata extractionknowledge basefailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecost reductioncycle time reductionemployee productivitytime savedvendor customer storymarketing opssales opsai draft human approval