Customer support

We Florida Financial Credit Union · interface.ai

Vendor-reportedBanking & financeUnited StatesScaledMid-marketNLP

Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.

Reported by interface.ai (vendor self-report). Checked against the source page on 2026-09-05. 4 of 4 figures below appear on that page word for word.

The problem

Preparing for a major core migration expected to significantly increase call volume, needing to manage higher call levels without increasing staff, and replacing an outdated IVR system.

What was deployed

Vendor
interface.ai
Products
Voice AI
Technique
NLP
Build or buy
Bought off the shelf
Deployment
SaaS
Data used
credit union's most common use cases, including balance inquiries, transaction history, password resets, and card-related issues
Scale
all inbound calls for 41,000+ members

What changed

Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.

13 agentssupport staff reductionfrom 16 agents

19% support staff reduction

Quoted word for word from the source

58%Call automation

The AI agent automates 58% of calls

Quoted word for word from the source

90-95%Answer rate

The contact center now consistently maintains a 90–95% answer rate

Quoted word for word from the source

2%missed calls rate

The contact center now consistently maintains a 90–95% answer rate, with only 2% missed calls

Quoted word for word from the source

We forecasted contact volume would jump exponentially. Voice AI became the solution to divert that traffic to protect our call center staff and ensure service levels remained high.
Aseef Amin, Member Relations Manager

Difficulties and limits

Sources

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