Caller: “I wanted an implant, but my dentist told me I don’t have enough bone. Is that even possible at your clinic?”
AI agent (average): “We offer top-tier care and the best specialists. Would you like to book a consultation?”
AI agent (the real one): “That’s a common scenario. I can’t promise a final answer over the phone because every case is different. But I can explain how we check this at our clinic. Have you had a 3D scan (CBCT) already, or only a standard X-ray?”
The difference? The first agent knows your services. The second knows your customers. The third - if you build it right - also knows your exceptions. And that’s where cost turns into profit.
After every webinar on voice AI agents, I hear the same question: “Which model are you using behind the scenes?” I get it. When something finally works, people look for the magic formula. The shortcut. The engine you can just buy and copy. But in the world of AI agents, the smartest model doesn’t win. The one that knows you best wins.
A useful AI agent isn’t a single variable. It’s the product of three:
The math is brutal: if CQ is near zero, the outcome is near zero - no matter how “smart” the model is. And that’s exactly where most projects fail.
Here’s a concrete number from the IMED Zagreb dental clinic: 43% of calls come from people seeking a solution for a complex case for the first time. They’re not customers yet. They’re leads with questions that don’t have a generic answer.
“Can I get an implant if I don’t have enough bone?”
“How much will it cost?”
“How long does it take?”
An AI agent without context (CQ) usually makes one of two mistakes:
A high-CQ agent does something else: it runs the process. It doesn’t diagnose and it doesn’t bluff. But it knows what needs to happen next: confirm what the person already has (X-ray/CBCT), explain the protocol, ask the right questions, and only then offer an appointment. That isn’t improvisation. Those are the rules of the game - built into your workflows. This is where CRM and CDP take on a new role. They’re no longer just “history.” They become a context vault - and the playbook for every agent who interacts with your customers.
Global tech giants will keep building better LLMs. That’s their league. Our league (FrodX and Kinetara.ai) is getting power to the wheels:
Without that, AI is just a new form of chaos. The agent quotes outdated pricing. The team loses trust. Departments spin up “Shadow AI.” And six months later everyone says: “AI doesn’t work.” AI works. It just doesn’t know who you are yet.
In 7 out of 10 projects we see in the market, companies can’t answer this clearly: What are the three things your agent must know about a caller before it even picks up the phone?
This isn’t a technology problem. It’s an ownership problem - because nobody owns that knowledge. So I’m not asking you which model you’d pick. I’m asking this:
Do you know which data your agent would need so it doesn’t sound like a call center from 2015?
Bring your most common sales call. In 20 minutes, we’ll identify the missing piece of context. No PowerPoint - just your case and a concrete answer.
Book 20 minutes here.
PS: If you want the full context and more real examples of building voice AI agents, I’m happy to walk you through them live - just reply and I’ll point you to the most relevant materials.