Eco-Friendly Product Advisor Chatbot
Plastic AI is an AI-powered chatbot that determines whether plastic products are eco-friendly and, when they are not, recommends sustainable alternatives from a curated knowledge base. Built to be scalable, intelligent, and cloud-native, it delivers accurate recommendations with minimal latency.
- Real-time eco validation
- Sustainable alternative recommendations
- Low-latency, cloud-native architecture
Client Overview
Plastic AI helps users identify whether plastic products are eco-friendly. If a product is not sustainable, the chatbot recommends alternative eco-friendly products from its curated knowledge base. The client required a scalable, intelligent, and cloud-native AI solution capable of handling real-time queries with high accuracy and low latency.
Business Challenge
Users and organizations lack quick, reliable information about the environmental impact of plastic products. Manual research is slow and inconsistent. The platform needed to assess eco-friendliness in real time, recommend sustainable alternatives, scale with demand, converse naturally across channels, and ensure accuracy, availability, and security.
Info Fragmentation
Eco data is scattered and hard to interpret quickly.
Manual Research
Time-consuming, inconsistent product sustainability checks.
Recommendation Gaps
Lack of automated eco-friendly alternatives.
Scalability & Latency
Growing traffic requiring low-latency responses.
Engagement Objectives
Design an AI chatbot for eco validation, deliver a recommendation engine, ensure cloud-native scalability, provide natural NLP conversations, enable web/mobile integration, and implement analytics for adoption and eco-impact.
Design and deploy an AI-powered chatbot to assess eco-friendliness.
Provide a recommendation engine for sustainable alternatives.
Ensure scalability and reliability via cloud-native architecture.
Deliver natural conversational experience using NLP models.
Enable multi-platform integration (web and mobile).
Implement analytics to measure adoption and eco-impact.
Solution Design
GenClouds developed Plastic AI as an intelligent virtual assistant leveraging AWS services and ML models. The solution included:
AI-driven NLP Chatbot
Understands product queries and performs eco validation in real time.
Recommendation Engine
Suggests sustainable alternatives from a curated knowledge base.
Omnichannel Deployment
Available across web and mobile apps for broader reach.
Serverless Architecture
Cost-efficient scaling and low-latency responses.
Advanced Analytics
Dashboards to monitor interactions, trends, and eco impact.
Enterprise-grade Security
Compliance-ready, secure data handling and access controls.
Solution Architecture
A serverless, AI-first design built on AWS for low latency, scalability, and security.
Platform Architecture
Core components across AI/NLP, compute, data, integration, UI, and security.
- AI & NLP: AWS Lex, custom ML models for eco validation
- Compute: AWS Lambda for serverless execution
- Database: Amazon DynamoDB for eco-product knowledge base
- Integration: AWS API Gateway for service orchestration
- Frontend: ReactJS for web chatbot interface; Backend: Node.js for recommendations
- Monitoring: AWS CloudWatch for logging and metrics
- Security: IAM roles, encrypted storage, secured APIs
Results & Outcomes
Plastic AI delivered measurable impact in validation speed, recommendations, adoption, efficiency, and scale.
Instant Eco Validation: Immediate assessment of product sustainability.
Recommendation Success: High-accuracy alternative suggestions.
User Adoption: Simplified sustainability decisions increased engagement.
Operational Efficiency: Serverless model reduced infrastructure costs by 40%.
Scalability: Designed to handle thousands of queries per day.
Sustainability Awareness: Raised knowledge of eco-friendly options.
Conclusion
By leveraging AI, NLP, and serverless AWS architecture, GenClouds delivered Plastic AI — a scalable, intelligent chatbot that simplifies eco-conscious decisions. The solution empowers users to adopt sustainable alternatives, demonstrating meaningful environmental impact while ensuring reliability, security, and cost efficiency.