Building an AI receptionist for efficient appointment scheduling and call handling: A Case Study

About AI Receptionist

With the advent of AI, more and more companies have started using intelligent solutions for managing their front desk operations. With manual handling of calls and appointments being extremely time-consuming and prone to errors, our expert developers at APPWRK leveraged the power of artificial intelligence to build an AI Receptionist that can take over the front desk and work around the clock while saving a substantial amount of money and time.

Packed with cutting-edge capabilities like real-time speech-to-text transcription, the AI receptionist is a virtual assistant powered by artificial intelligence and a blend of technologies like Open AI, Deepgram AI, Twilio, Node.js, GPT 3.5 Turbo and Llama3 – 70B to interact with people, manage scheduling and respond to routine inquiries.

Industry

Artificial Intelligence
Application Type

Artificial Intelligence Tool
Services

AI Development
Team

Project Manager, AI Engineer, UI/UX Designer, Developer
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The Goal of Building the AI Receptionist

The AI Receptionist was designed to handle many of the tasks traditionally performed by a human receptionist, namely connecting with users, accurately understanding user queries and ensuring smooth appointment management. With custom prompt engineering and lightning-fast response times of just 500 to 800 milliseconds, the AI receptionist was developed to serve as a highly scalable and powerful tool. Designed for seamless appointment scheduling and management, it can be efficiently deployed across various industries and domains, transforming the way businesses handle customer interactions.

Use Cases:

Recruitment & HR Firms

The AI receptionist can streamline interview scheduling for recruitment, handling high volumes of calls and ensuring seamless coordination between candidates and recruiters.

Healthcare

The AI receptionist can help medical centers schedule and reschedule appointments with doctors effortlessly, ensuring they do not overlap each other.

Legal & Consultancy Firms

The AI receptionist manages scheduling client consultations and follow-ups, updating lawyers and consultants efficiently so that they never miss important case discussions.

Hotels & Hospitality

With the AI receptionist, guests can book stays, modify reservations, and inquire about availability 24/7, greatly reducing the workload on human receptionists.

E-commerce & Retail Stores

Customers can schedule in-store pickups and consultations with sales representatives through the AI receptionist, ensuring smooth appointment management.

Technologies Used

Open AI
Deepgram AI
Twilio
Node.js
GPT 3.5 Turbo
Llama 3 - 70B

Challenges Faced During the Development of the AI-Receptionist

1. Minimizing Response Latency to Achieve a Human-Like Interaction with Users

Ensuring that the AI Receptionist delivered a truly human-like interaction was a major challenge as that required bringing down the response latency to less than a few seconds. Our team had to work on eliminating the commonly found “AI lag”, which tends to break the flow and remove any artificiality in the conversation and make it seem more natural and human-like.

2. Preventing Interruptions and Speech Overlaps by Accurately Detecting User Pauses

One of the major hurdles our team faced while developing the AI Receptionist was ensuring that it did not interrupt users mid-conversation or create speech overlaps, which could lead to a frustrating and unnatural user experience. Since AI systems operate in a fixed and predefined manner, we had to come up with a solution that enabled the tool to dynamically adjust its response timing based on the user’s speech speed, emotional tone, or pauses.

3. Accurately Parsing Multiple Languages for Clear and Effective Communication

The AI Receptionist had to dynamically recognize and accurately process multiple predefined languages. It had to intelligently map speech variations, including dialects, accents, gender-based voice variations, and individual speech patterns and transcribe them accurately to avoid any sort of misinterpretation that could lead to incorrect appointment scheduling or user frustration.

4. Maintaining Scope-Restricted Responses and Avoiding Irrelevant Queries

Our team had to ensure that the AI Receptionist remained focused on the topic and did not generate any irrelevant or out-of-scope responses by coming up with a solution to refine its query filtering mechanisms.

Solutions

01

In order to avoid any interruptions and eliminate speech overlaps, our team integrated a custom-coded speech detector with real-time speech analysis that accurately identified when a user was actively speaking and when they had finished their sentence. This ensured the AI Receptionist waited for the right moment to respond rather than cutting off the user mid-sentence, greatly enhancing the user experience.
02

To ensure a highly accurate speech-to-text transcription, we integrated Google APIs, OpenAI APIs and Deepgram APIs into the AI Receptionist. Since Google and Deepgram’s Speech-to-Text APIs provide state-of-the-art voice recognition and OpenAI’s language models enhance contextual understanding, the powerful combination of their APIs allowed the AI Receptionist to minimize errors and improve comprehension.
03

Our expert developers at APPWRK extensively worked on optimising the backend to ensure the AI Receptionist processed speech by breaking it into manageable chunks, allowing it to analyze and prepare responses dynamically. Instead of waiting for the user to finish speaking before processing their query, the AI receptionist continuously processed the speech in the background, which significantly reduced the response latency to 500 to 800 milliseconds.
04

Our team prioritized security by implementing several industry best practices like end-to-end encryption along with SSM and SSL authentication, ensuring that every user interaction remained highly protected against breaches and unauthorized access. By employing this framework, we managed to not only keep the AI Receptionist highly secure but also guarantee confidentiality to the users.

Key Results

Integrated OpenAI API, Deepgram APIs, and Twilio, which enabled highly accurate speech-to-text and text-to-speech processing, easy comprehension and efficient management of calls from users.
Integrated Google Calendar to enable real-time appointment scheduling, rescheduling, and updates, dynamically filling slots to keep schedules organized and up to date.
Utilized a powerful combination of GPT-3.5 Turbo and LLAMA 3 (70B) that enhanced natural, human-like conversations, contextual awareness, multi-language support, and scalability, making interactions smoother and more intuitive.
The AI Receptionist was finely trained using large language models like GPT 3.5 turbo and LLAMA 3 (70B) and advanced prompt engineering techniques with a predefined set of queries, which made it deliver highly precise and consistent responses tailored to user needs.
Implemented Guard models to prevent the AI Receptionist from responding to irrelevant or out-of-scope queries, ensuring focused and context-aware user interactions.