The use case: how Whop deploys Decagon
Whop uses Decagon's AI chat agent to answer customer support tickets, tailoring responses using user and product usage metadata. Decagon also tags each ticket to a product surface, which the company uses to spot friction and guide product fixes.
More reported results
Figures published in Decagon’s case study. They are not independently measured by usedby.
Payments, once the number one pain point, saw tickets decrease by 50% after Decagon’s insights guided targeted fixes.
In the customer’s words
Continuously hiring staff to answer the same questions isn’t efficient. We needed an AI solution to deliver higher-quality responses and let our team focus on higher-impact problems.Other AI tools used by Whop
Other public relationships recorded on usedby.
Explore Whop’s AI stack
Decagon users in Marketplace
Companies in the same sector, with public evidence.
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Go straight to the source
Based on Decagon’s published case study. We check the public evidence; the results are reported by the vendor.
decagon.ai- Source published
- Sep 11, 2026
- Last checked
- Oct 7, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 65–70%Checked
The figure is on the page; its label is our wording.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Whop.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- Whop uses DecagonChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
By implementing Decagon, Whop quickly achieved 65–70% ticket deflection rates. This freed the support team to focus on complex cases while giving users fast, accurate answers.