- Healthcare / DentalIndustry
- AI AgentsCategory
- Fast to valueTime to impact
The challenge
- Front desks miss calls during busy periods and after hours, and every missed call can be a missed appointment.
- Routine questions and scheduling eat up staff time that could go to patients in the chair.
A voice agent that answers patients and books appointments in natural conversation.
A conversational voice assistant for dental clinics that handles common patient questions and books appointments, including outside front-desk hours.
What we built
- 01A natural voice interface lets patients speak as they normally would.
- 02The assistant answers frequent questions about the clinic, services and hours.
- 03It books, reschedules and confirms appointments conversationally.
- 04It covers after-hours demand so no enquiry goes unanswered.
How it works
What it can do
Natural voice interface
Patients talk normally, no menus or keypad trees.
Appointment booking
Books, reschedules and confirms in conversation.
Patient FAQ
Answers common questions about the clinic and services.
After-hours coverage
Captures demand when the front desk is closed.
Who stays in the loop
The call the system deliberately does not take, and what the person is given to take it with.
A conversational voice assistant for dental clinics that handles common patient questions and books appointments
The front desk
Decidesanything clinical, and any call the agent could not close
Seesthe transcript, what the patient asked for, and the slot the agent was about to book
The payoff
What is different once it is running. No invented numbers: these are the changes the work was built to make.
Every call gets answered, even at lunch or after hours, and turns into a booked appointment instead of a missed one.
Fewer missed calls, so fewer missed appointments.
Routine questions and scheduling come off the front desk’s plate.
Patients book whenever they think of it, not just nine to five.
Built with
The layers this runs on, from what comes in to what it plugs into.
- Model layer
- Voice AI
- LLM
- What people use
- Web
- Connects to
- Google Auth
Common questions
The same answers as above, written out.
A conversational voice assistant for dental clinics that handles common patient questions and books appointments, including outside front-desk hours.
Front desks miss calls during busy periods and after hours, and every missed call can be a missed appointment. Routine questions and scheduling eat up staff time that could go to patients in the chair.
A patient calls and speaks naturally. The agent answers questions or books into the calendar. The appointment is confirmed, any hour of the day.
The front desk decides anything clinical, and any call the agent could not close. They see the transcript, what the patient asked for, and the slot the agent was about to book.
Model layer: Voice AI, LLM. What people use: Web. Connects to: Google Auth.
Fewer missed calls, so fewer missed appointments. Routine questions and scheduling come off the front desk’s plate. Patients book whenever they think of it, not just nine to five.
Where this fits
The practice it belongs to, how an engagement like it runs, and what we have written about the subject.
Artificial IntelligenceArchitecting AI Agent Systems: A Strategic Framework for Production Deployment
Choosing the right LLM framework is a strategic business decision that determines scalability, cost control, and system resilience. Learn how to handle the trade-offs between speed, flexibility, and governance when building production-grade AI automation.
Frederico VicenteNov 202512 min read
Artificial IntelligenceModel Context Protocol: Standardizing Context and Tool Integration for Agentic AI
As LLMs evolve from stateless prompt responders to stateful, tool-using agents, fragile hand-wired orchestration is breaking down. MCP provides a vendor-neutral protocol for connecting models with structured context, tools, and external systems at runtime.
Frederico VicenteNov 202515 min read
Business & OperationsOptimising your operations with AI: start with the process, not the model
A practical playbook for optimising operations with AI: find the one slow step, automate it, keep a person in the loop, and measure the before and after.
Miguel Vicente JrJun 202612 min read
Other projects in this line.
Bring us the problem nobody has cracked yet.
We are a small team of senior specialists. We pick the right model and the right layer, and we build the least machinery that does the job. You get a call with an engineer, not a sales deck.