Project Overview:
I led the development of an AI-powered chatbot designed to enhance customer service and automate routine inquiries. My role involved conceptualizing the chatbot’s functionality, setting up the foundational technology, and collaborating with AWS experts to ensure the project met our company’s specific requirements. The chatbot was intended to streamline internal interactions, reduce response times, and provide 24/7 support, aligning with company goals needs.
Steps to Complete the Project:
1. Conceptualize the Chatbot Based on Business Needs
- Objective: Automate internal tasks and provide round-the-clock support using AI-powered technology.
- Goal: Create a chatbot that can handle internal inquiries, provide information, and guide users through our product offerings.
- Feedback: Gathered detailed input from the CEO and leadership team to understand the company’s vision, internal pain points, and overall business goals.
- Deliverables: Define key chatbot functionalities such as FAQs, product guidance, and escalation for complex queries.
2. Initial Technology Research & Setup
- Conduct research on the latest AI technologies and chatbot frameworks to determine the best options for our project.
- Explore cloud-based solutions that could support the AI chatbot, focusing on scalability and cost-effectiveness.
- Set up the basic technological framework to showcase potential chatbot workflows and integrations with existing customer service platforms.
3. Collaborate with AWS Team to Refine Requirements
- Partnership: Initiated discussions with the AWS team to evaluate their cloud infrastructure, AI services, and backend support capabilities.
- Needs Analysis: Provided the AWS team with our specific technical and business requirements, based on feedback from the CEO, such as the need for 24/7 support, minimal human intervention, and easy integration with our CRM.
- Key Features: Ensured the chatbot could handle natural language processing (NLP), seamlessly escalate more complex queries to human agents, and provide analytics on internal interactions.
4. Negotiate Cloud Infrastructure and Budget
- Budgeting: Worked with the AWS team to negotiate a cost-effective backend solution that aligned with our company’s budget. This included discussing AWS services pricing for AI, storage, and compute resources required for the chatbot.
- Scalability: Ensured the proposed solution could scale with the company’s growth, handling an increasing volume of customer queries without additional overhead costs.
- Support: Negotiated backend support and ongoing service agreements, ensuring we had the necessary AWS technical assistance during the implementation and scaling phases.
5. Design Workflows and User Experience
- Mapped out internal interaction workflows to determine how the chatbot would respond to various inquiries.
- Collaborated with the IT team to ensure the chatbot’s interactions were user-friendly and in line with company’s expectations.
- Designed escalation workflows for instances when the chatbot could not resolve customer issues and needed to hand off to a human agent.
6. Oversee Development and Implementation
- Led cross-functional teams of engineers and customer service representatives to bring the chatbot to life, ensuring alignment between the technical build and business goals.
- Provided ongoing feedback to the development team to ensure the chatbot’s functionality met the specified requirements.
- Ensured thorough testing of the chatbot in a live environment, collecting feedback from both internal stakeholders and test customers.
7. Monitor & Optimize Performance
- Monitored the chatbot’s performance post-launch, gathering metrics on response times, accuracy, and customer satisfaction.
- Worked with the AWS team to continuously optimize the backend infrastructure, ensuring the chatbot ran efficiently with minimal downtime.
- Conducted regular reviews with internal team to identify areas for improvement and adjust the chatbot’s functionality based on feedback.
8. Present Outcomes to Leadership
- Delivered reports to the CEO and leadership team outlining the chatbot’s performance metrics, including customer satisfaction rates, reductions in response times, and cost savings from automation.
- Highlighted the success of the AWS integration in scaling the chatbot and ensuring seamless customer service.
- Presented a roadmap for future enhancements and additional features based on evolving customer needs and company growth.
Software & Tools Used:
- AWS Cloud Services (Amazon Claude): For cloud-based AI chatbot infrastructure, storage, and compute resources.
- Natural Language Processing (NLP): For enabling the chatbot to understand and respond to customer queries.
- Trello: For managing the project and tracking development milestones.
- Teams: For team communication and updates during the project.
- GitHub: For version control, file sharing, and collaboration with the development team.