About the Project
Inspiration
This project was inspired by a simple but costly problem faced by many small and medium-sized businesses: missed customer calls.
Restaurants, hotels, clinics, and service-based businesses often lose potential customers because no one is available to answer the phone. During busy hours, staff are focused on serving in-person customers, and after business hours, calls are usually missed or sent to voicemail. In many cases, these missed calls directly translate into lost revenue.
We wanted to build something that behaves like a real receptionist—one that never sleeps, never misses a call, and can handle multiple customers at once.
That is how the idea of an AI Receptionist was born.
What We Built
We built an AI-powered phone receptionist that answers customer calls, collects booking information, and stores it for business owners to review.
When a customer calls, the AI:
- Answers the phone immediately
- Introduces itself as an AI assistant
- Explains that the call is being recorded for booking purposes
- Asks for relevant information based on the business type (e.g., hotel, restaurant, clinic)
- Collects structured booking details such as name, date, time, and service type
- Repeats the key details back to the customer for confirmation
- Ends the call politely
After the call, the system:
- Stores the collected booking data in a structured format
- Generates a call summary
- Sends confirmation via email or SMS (if enabled)
- Displays the booking in a business dashboard for staff to review the next day
Each business can also customize the questions based on their workflow and industry needs.
How We Built It
The system is designed around an AI agent that handles real-time voice conversations.
Key components include:
- Speech-to-Text for converting customer speech into text
- Gemini / LLM-based agent for conversation reasoning and response generation
- Text-to-Speech for generating natural spoken responses
- Backend service to manage call sessions and store booking data
- Database to store customer requests and call logs
- Simple web dashboard for businesses to view incoming bookings
- Telephony integration (e.g., Twilio or similar service) to handle incoming calls
We designed the system so that the AI is not just answering questions, but actively guiding the conversation toward collecting structured booking information.
Challenges We Faced
One of the biggest challenges was making the AI handle real-time phone conversations smoothly.
Unlike chat-based systems, phone calls require:
- Fast response times
- Natural turn-taking
- Clear and concise speech
- Handling interruptions or incomplete answers
Another challenge was converting unstructured human speech into structured booking data. Customers rarely provide information in a clean format, so we had to design prompts and logic to continuously extract and update structured fields during the conversation.
We also had to balance automation with reliability. In the basic version, the AI focuses on collecting data, while humans still validate bookings before final confirmation.
What We Learned
This project helped us understand how AI systems move beyond chatbots into real operational tools.
We learned:
- How to design AI agents that perform real tasks, not just conversations
- The importance of structured data extraction in real-world AI systems
- The complexity of real-time voice interaction compared to chat-based AI
- How small automation steps can create significant business value
Most importantly, we learned that AI is most powerful when it removes repetitive administrative work rather than trying to fully replace humans.
Built With
- Gemini / Large Language Models
- Speech-to-Text APIs
- Text-to-Speech APIs
- Google Cloud Platform (backend + hosting)
- Database (Firestore / SQL)
- Web dashboard (React / Next.js)
- Telephony API (e.g., Twilio or similar)
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