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Abhishek R  • Peerlist

Developed a versatile Jarvis-style chatbot using JavaScript, which can perform various tasks and respond interactively to user inputs. The chatbot employs Natural Language Processing (NLP) to understand user commands and integrates multiple APIs to provide features like weather updates, news, and utilities. The project includes voice recognition and text-to-speech capabilities, enhancing user interaction and experience. Built with a dynamic interface using HTML, CSS, and JavaScript, the chatbot is designed to be intuitive and responsive, offering a seamless user experience. Extensive testing and optimization were carried out to ensure performance and reliability.

Jarvis Chat-Bot Java Script

Designed a full product spec for an LLM-powered job matching engine for Naukri — addressing India's broken job market from a PM's perspective. Covers: Problem statement · User personas · AI architecture · Success metrics · Risk analysis · 3-phase rollout plan Read the full case study →

NaukriAI Match Engine — AI PM Case Study

Developed an online DJ booking management system to streamline the process of booking DJs for events. The system features a user-friendly interface where clients can browse DJ profiles, check availability, and make bookings. Built with a robust backend using PHP and MySQL, the system ensures secure data management and real-time updates. Implemented features include automated booking confirmations, payment processing, and an admin panel for managing DJ profiles and bookings. The responsive design guarantees accessibility on various devices. This project enhances efficiency and convenience for both clients and DJs, providing a seamless booking experience.

Online DJ Booking Management system

Developed a machine learning model to detect fraudulent online payment transactions, leveraging various algorithms and data preprocessing techniques to achieve high accuracy. The project involved collecting and cleaning transaction data, encoding categorical variables, and normalizing numerical features. Implemented and optimized multiple algorithms, including Logistic Regression, Decision Trees, Random Forest, and Neural Networks. Evaluated model performance using metrics like Accuracy, Precision, Recall, F1 Score, and ROC-AUC Curve. The project resulted in a robust fraud detection system that significantly improves the identification of fraudulent activities in online payments.

Online Payment Fraud Detection using Machine 

Designed and developed a personal portfolio website to showcase projects, skills, and professional achievements. The website features a clean and modern user interface, built with HTML, CSS, and JavaScript. It includes sections for an about me, project showcases, blog, and contact form. The responsive design ensures optimal viewing on various devices. Implemented SEO best practices to enhance visibility and integrated Google Analytics for tracking visitor engagement. The website serves as an effective platform to highlight my work, attract potential clients, and connect with industry professionals.

Portfolio website

Built a full-stack AI product that generates professional Product Requirements Documents (PRDs) from a simple product idea in under 60 seconds. PRDForge auto-generates Executive Summary, Problem Statement, Goals, User Personas, User Stories, Feature Requirements with RICE scoring, Edge Cases, and Out of Scope — structured exactly how top PMs write them. Key highlights: Designed end-to-end product thinking, user flow, and feature prioritization Integrated AI to generate context-aware, role-specific PRD content Built export to PDF functionality for real team usage Used by early-career PMs to ship faster and communicate better with engineering Tech & Tools: Lovable, AI APIs, PDF Export

PRDForge — AI-Powered PRD Generator

Conducted an in-depth analysis of the impact of R&D spend, administration costs, and marketing spends on company performance. Utilized data analysis and machine learning techniques to identify significant patterns and correlations. The project involved data cleaning, exploratory data analysis, correlation analysis, and regression modeling. Key findings highlighted the relative importance of each spending category and provided actionable insights for optimizing budget allocations. The analysis leveraged Python libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and Seaborn, and was documented in a comprehensive Jupyter Notebook.

R-and-D-spend-Administration-Cost-and-marketi

Building an AI-powered CRO tool that personalizes landing pages in real-time based on visitor intent signals. Product features: → URL preview rendering using Microlink API + proxy architecture → LLM-powered content personalization layer → A/B testing framework for conversion optimization Live demo: troopod-enhance.emergent.host GitHub: github.com/Abhishek370914