Abhishek Chaturvedi

Led a team to integrate a product recommendation system with customer feedback, leveraging the Langchain framework with databases such as ChromaDB and PGVector for optimal performance. Designed and enhanced a responsive chatbot using React, langchain, and OpenAI, achieving a 92 accuracy rate through iterative prompt training. This versatile chatbot handles diverse data inputs – documents, databases, URLs, presentations, and images – transforming customer engagement. Implemented a large-scale enterprise search solution using AWS Kendra, optimizing search capabilities across 1,000+ PDFs for enhanced data retrieval and user experience. Tailored chatbot solutions to align with brand strategies, ensuring robust security and the highest standards of data protection.

Designed and deployed a serverless API on AWS Lambda, using python events for seamless event-driven functionality. Integrated with AWS gateway API to provide scalable and efficient endpoints, optimizing cloud resource usage. Successfully automated Salesforce, Guidewire, and Symbility integrations through the use of the MuleSoft API and achieved a 50% reduction in data updation time. Successfully implemented CI/CD processes using GitHub flows on the MuleSoft anypoint platform. This enabled streamlined and automated code deployment workflows, enhancing development efficiency and improving project delivery. Collaborated with multiple stakeholders to gather their requirements, led the end-to-end design of the integration architecture, and conducted thorough testing across various scenarios.

Developed and Trained Machine Learning generic model using Regression model Techniques. Determined optimal strategies to understand the customer behavior during product launch and facilitate the targeted market campaigns. Worked on a targeted algorithm to acquire an increase in market share by developing a customer segmentation algorithm. Developed and designed an engine from scratch for customer communication via applications and upgraded our centralized ILESB services for hassle-free integration. Finalised business requirement and delivered the engine within the minimum time possible using technologies like Django and PostgreSQL. The engine improved the overall performance ratio from 98.8% to 99.4% and also decreased the turnaround time for message delivery.