Griswold Houston
How Zingage adapts to individual client needs—even in high-stress situations
The Situation
A No-Show, An Upset Client, and a Learning Moment
No-call no-shows are every agency's nightmare—for the client left without care and for the team scrambling to respond. When one occurred at Griswold Houston on a Saturday morning, it triggered exactly the kind of high-stress interaction that tests any system.
The client's daughter called in, understandably upset. Her mother's caregiver hadn't shown up, and she was speaking to an AI.
Brenda Gross, Care Coordinator at Griswold Houston, was monitoring the situation: "We had a very upset client today. She sent us a text saying she's likely to cancel services if she has to talk to AI when our caregiver no-call no-shows."
The situation was escalated to a human agent, as it should have been. But what happened next demonstrated the system's intelligence.
"I am going through the logs and do see where AI noted a preference for this client to speak with a live agent. It looks as though maybe your system is automatically going to update that—that's impressive!"
— Brenda Gross, Care Coordinator
Adaptive Preference Learning
Zingage's AI doesn't just handle calls—it learns from them. When the upset client expressed a clear preference for human interaction during emergencies, the system:
Recognized the preference during the live interaction
Documented it automatically in the client's profile
Will apply it to future calls without manual configuration
"The fact that it can learn people's preferences on the fly is very cool and I think will make a big difference," Brenda noted.
This isn't about AI trying to handle everything—it's about AI knowing when not to handle something and routing appropriately.
Human + AI Working Together
Victor Hunt, Zingage CEO, reviewed the interaction: "Our process is always to answer the call with AI, then determine the reason for the call, and then transfer as needed immediately. In this case, the call was transferred to our staff once it was determined that this was a no-show and the client had requested a person."
Brenda's reflection captured the right framing: "I am not suggesting humans do everything—maybe just take the call to ensure we're working on it. People like assurance from a human for that type of urgent situation. We should have worked that into our SOP. Not on you guys."
The outcome: The preference was captured. Future no-show situations for this client will route directly to a human agent. And the agency learned something about their own SOPs in the process.
Industry
Home Care (Mixed Payer)
Location
Houston, Texas
Primary Payer
Mixed
Use Case
After-Hours
"People will start to embrace it once they realize it gets the job done. I recall the same happening when I went from all phone contact to texting. Now everyone prefers texting."
Brenda Gross
Care Coordinator
Key Learnings
AI should know its limits
The best AI systems know when to hand off to humans.
Preferences compound over time
Every interaction makes the system smarter for that client.
SOPs evolve
Technology implementations often reveal gaps in existing processes.
© 2026 Zingage Inc.