project-proposal-2025

SmartCall AI

Abstract

Small businesses often face challenges in managing customer inquiries during peak times or after hours, which can lead to missed opportunities and poor customer experience. SmartCall AI is designed to help by automating phone interactions, handling tasks like answering questions, booking appointments, taking orders, and providing product information-all without needing human intervention. With seamless integration into existing phone systems, SmartCall AI aims to provide an affordable, easy-to-implement solution that offers customer support around the clock. Using AI-driven speech recognition and natural language processing, it delivers efficient, human like support boosting customer satisfaction and allowing businesses to focus on tasks that matter the most.

Author

Name: Shivam Garg

Student number: 47280647

Functionality

The SmartCall AI system is designed to help businesses automate call answering by acting as a virtual receptionist. By leveraging Natural Language Processing (NLP) and AI-driven decision-making, it can offer accurate information, manage scheduling, and escalate complex queries when needed.

Overview of various Flows:

1. Business Setup and Configuration:
   • Business owners sign up and configure call-handling preferences.
   • Integrate AI with existing database (such as menus), scheduling tools, and POS systems.
   • Customize response templates and decision tress for different scenarios based on business preferences.
2. Customer Call Handling:
   • The AI answers incoming calls and greets the customer.
   • The AI detects the intent of the customer (eg., placing an order, scheduling an appointment etc.)
   • Retrieves relevant information if request matches one of the decision trees.
   • Transfers complex or unknown requests to human representatives or schedules a callback when human intervention is not possible.
3. Follow-up and Logging:
   • The AI transcribes the entire conversation and summarizes key details.
   • Send automated follow-up messages (eg., order confirmations)
   • Business owners can access the transcribed logs and review conversations if required.

Features

• Setup and Configuration - The entire system is designed for commonly used SAAS systems so integration with businesses is smooth.
• Automated Call Answering - AI answers incoming calls, greets customers, and navigates through conversations with minimal human intervention.
• Intelligent Query Handling - The AI uses NLP to understand context and user intent, extract key information, and provide real-time responses.
• Business Data Integration - The AI takes into context relevant business information such as operating hours, appointment availability, and customer records from connected databases while making the interactions.
• Customizable Call Flows - Businesses can easily configure structured response flows, decision tress, and pre-recorded messages for personalized interactions.
• Call Prioritization and Escalation - The system is able to categorize calls based on urgency and can escalate critical calls to human representatives when needed.
• Scalability and Multi-calling Handling - The system can support simultaneous call processing, making it suitable for fast-growing businesses.
• Follow-up Messaging - The system can send personalized response on customer specified communication channel (can be messages, emails etc).
• Call Logging and Summaries - Stores all conversation transcripts and generates conversation summaries for business owners to review later.

Scope

To deliver a MVP, the system will at least need to perform the following key functionalities:

• Automated Call Answering

  The system must automatically answer incoming calls and respond appropriately using speech recognition software. The input could be handled using software such as Google Speech-to-text or AWS Transcribe.

• Natural Language Processing (NPL) for Query Handling

  The system needs to integrate a NLP engine such as the Google Dialogflow or OpenAI GPT-4 to process customer queries. This part of the system will be able to identify the customer’s intent behind every call and provide appropriate response based on predefined business logic.

• Call Escalation and Transfer

  The system must be able to detect complex or unresolved queries and request human intervention whenever needed.

• Call Logging and Summarization

  The system must be able to transcribe all conversations, create concise summaries and store them in a database such as Firebase.

• Admin Dashboard

  The system must include an admin dashboard which can be used by business workers to monitor ongoing calls, access call logs, and configure system settings. This could be achieved with a simple PWA using React with the backend built using Express.

This MVP will provide a system with core functionalities required to automate call answering, saving businesses crucial time during peak hours. Future enhancements can include multi-language support, advanced analytics and one-click integrations with existing POS systems.

Quality Attributes

Availability

Availability refers to the system’s ability to be accessible when in demand by the end-users. For SmartCall AI, this would mean the system is operational 24/7, with minimal to no downtime. This can be measured by keeping a track of uptime of the system. Measurable Criteria:

• A high percentage (around 95%) of uptime annually.
• System response for each call should be less than 5 seconds.
• Call failure rate should not exceed 5% per day.

Deployability

Deployability refers to how easily the system can be deployed, updated and scaled as needed. For SmartCall AI, the system must be easy to deploy even for small businesses with less hardware support and support seamless updates to the software without causing any major disruptions to ongoing operations. The system must be easy to deploy, easy to scale and capable of handling increasing user demand. Measurable Criteria:

• The system should be deployable even on low end hardware to make it accessible for small businesses.
• New features or updates should be deployable with minimal disruptions (under 30 minutes of downtime).

Reliability

Reliability ensures the system consistently delivers its functionality without failure. For SmartCall AI, this would mean ensuring that calls are either answered promptly and accurately every time or are transferred for human intervention in case of a failure. Measurable Criteria:

• No more than 5% of calls get passed for human intervention due to failure to answer.
• At least 95% uptime annually (excluding planned downtimes).
• Recovery from failures without needing for intervention from development team.

Scalability

Scalability ensures the system is simultaneously usable by a large number of end users and does not cost the business huge investment to deliver with varying user loads. Measurable Criteria:

• Ability to handle 500 concurrent calls with performance issues.
• Auto scaling for resources when usage exceeds 75% of potential capacity.
• Call response time under 5 seconds during peak loads.

Evaluation

Availability

To evaluate availability:

• Simulate user traffic using K6 and monitor system uptime to ensure the uptime percentages.
• Measure call response time using performance monitoring tools to ensure its stays below 5 seconds.
• Use logs to track and analyze call failure rates, making sure they remain below 5% per day.

Deployability

To assess Deployability:

• Test deployment and update times by measuring the downtime during updates ensuring it stays below 30 minutes.
• Deploy the system on different architectures (windows, Linux, MacOS) to make sure it works on all different platforms.
• Use load testing to check if the system scales during peak times, making sure there are no performance issues when scaling on the fly.

Reliability

To test Reliability:

• Simulate failure scenarios (such as network failure, server crash etc) and make sure the system recovers automatically without needing intervention from development team.
• Run unit tests and integration tests under normal and high-stress conditions ensuring system’s stability.

Scalability

Scalability can be tested by:

• Monitor auto-scaling though cloud provider tools depending on the architecture used (such as AWS auto scaling) when the usage exceeds 75% of capacity.
• Conduct stress tests using K6 to simulate 500 concurrent calls making sure the system works fine under heavy loads.
• Measure call response times during peak loads using APM tools like New Relic to make sure call response times stay below 5 seconds.