The use case: how Valon deploys Decagon
Valon, a mortgage servicer, uses Decagon AI voice agents to handle inbound homeowner calls end-to-end, absorbing volume spikes without overstaffing while meeting accuracy and compliance requirements. It also uses Decagon for chat and email, and builds new use cases and call flows itself with Agent Operating Procedures.
More reported results
Figures published in Decagon’s case study. They are not independently measured by usedby.
CSAT was maintained at ~90%, indicating that AI-driven deflection didn’t compromise customer experience.
Achieved 50%+ deflection on voice for inbound calls, even when onboarding 80,000 loans in a single month.
In the customer’s words
Decagon showed us that speed and compliance don’t have to be at odds. They navigated the regulatory challenges of mortgage servicing while keeping up with Valon’s pace of innovation, driving higher deflection and sustaining a great customer experience.Other AI tools used by Valon
Other public relationships recorded on usedby.
Explore Valon’s AI stack
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FaireConfirmedGo 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
- Jul 28, 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: 50%+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 Valon.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- Valon uses DecagonChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
Achieved 50%+ deflection on voice for inbound calls, even when onboarding 80,000 loans in a single month.