As a software engineer, I’m always looking for new challenges to test my skills. Recently, I challenged myself to create an AI-powered WhatsApp assistant in just four days. The result of this challenge was Gnius.ai, a WhatsApp assistant that uses artificial intelligence to answer users’ questions and provide personalized recommendations.
In this article, I’ll share the process I followed to create Gnius.ai, including the technologies I employed, the challenges I faced, and how I designed the platform using Figma.
Day 1: Planning and Research
On the first day, I spent most of my time planning and researching. I started by brainstorming ideas for an AI-powered WhatsApp assistant that could be developed within four days. I eventually settled on the idea of a platform that could provide personalized recommendations to users based on their queries.
After the research, I began exploring the various technologies and tools available for building the platform. I chose NodeJS, TypeScript, and MongoDB for the backend, OpenAI for the artificial intelligence, and Twilio for the WhatsApp integration.
I also started designing the platform using Figma, a collaborative design tool. I created wireframes and a rough layout for the landing page.
Day 2: Development
With my plan in place, I started development on the second day. I began by setting up a basic NodeJS and TypeScript backend, including the integration with MongoDB and Twilio.
Next, I integrated the OpenAI GPT-3 model into the platform, allowing users to input their queries and receive personalized recommendations generated by the AI.
One of the biggest challenges I faced during development was figuring out how to integrate the Twilio API with the backend. It took some trial and error, but I eventually found a solution that worked.
To ensure that any potential errors would be quickly identified and addressed, I also added Sentry, an error monitoring service, to the platform.
To cover the cost of using various APIs and ensure the continued development of Gnius.ai, I integrated Stripe, a popular payment gateway, into the platform. With Stripe, users can subscribe to Gnius.ai on a monthly basis and enjoy unlimited messaging. The revenue generated from subscriptions will ensure the long-term viability of the platform.
Day 3: Testing and Refinement
On the third day, I spent most of my time testing and refining the platform. I used a combination of manual testing and automated testing using tools like Postman to ensure that the platform was functioning as intended.
I also spent time refining the AI model, fine-tuning it to provide more accurate and relevant recommendations based on user queries.
During this phase, I also continued to refine the design of the landing page using Figma, incorporating feedback from early testers and improving the user interface to make it more intuitive and user-friendly.
Day 4: Deployment and Launch
On the final day, I focused on deployment and launch. I deployed the platform to a cloud server using Heroku and then launched it to the public.
To gain insight into user behavior and preferences, I incorporated Segment for tracking. This provided me with data on how users were utilizing the platform. By analyzing this data, I am able to further refine the AI model and enhance the overall user experience.
The launch was a success, and I received positive feedback from users who appreciated the personalized recommendations provided by the AI.
Next Steps
There’s always room for improvement in any project. In the future, I plan to add audio and image support to Gnius.ai, which will make it even more versatile and enhance the user experience. However, since Gnius.ai depends on OpenAI’s third-party API, I may occasionally face slower-than-expected responses. To address this, I’ll continue to monitor the API’s performance and explore ways to optimize its integration with Gnius.ai.
Additionally, I’ll keep a close eye on user feedback and make iterative improvements to the platform’s AI model to ensure that it stays accurate and relevant. By being responsive to user needs and leveraging new technologies, I’m confident that Gnius.ai will continue to evolve and grow in exciting new ways.
Conclusion
Creating Gnius.ai, an AI-powered WhatsApp assistant, in just four days was a challenging but rewarding experience. By exploring various technologies and tools, conducting user experience research, and designing the landing page using Figma, I was able to develop a functional platform that can help users get personalized recommendations quickly and easily.
If you’re thinking of embarking on a similar challenge, my advice would be to start with a clear plan, conduct thorough user research, and be willing to experiment with different technologies and tools until you find what works best for you. It’s also important to focus on the user experience and design a platform that is intuitive and easy to use.
Finally, don’t be afraid to seek feedback from early testers and incorporate their suggestions into your design and development process. With these tips in mind, you too can create a successful AI-powered platform in a short amount of time.
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