This project involves creating analytical reports and dashboards for retail sales and healthcare metrics. The store sales report tracks sales performance using key metrics such as total sales, targets, and period-based sales comparisons, with filters for date and location. The repair summary report provides insights into repair activities with visual charts and multiple filters. The top sales report highlights salesperson performance, while the top sellers report lists best-selling products. In healthcare, dashboards focus on patient-related metrics, engagement, and group analysis, offering insights into appointments, measures, and patient interactions with various filtering options for effective data analysis and decision-making.

An AI-first, Gen Z–oriented fashion styling app enabling users to upload wardrobe items, receive personalized outfit suggestions using AI, and engage socially via outfit polls and feeds. The solution leverages Firebase’s scalable cloud backend and integrates OpenAI for prompt-based outfit generation. Users can manage a virtual closet, receive AI alerts on missing wardrobe items, and interact with a community through polls and comments. It also includes an admin panel for moderation, analytics, and subscription management. Monetization is driven through subscription tiers and integrated ads. The product is designed as a progressive MVP with clearly defined “must-have” and “nice-to-have” features to facilitate phased rollout. Post-launch, the client aims to validate market fit and scale with affiliate shopping and influencer integrations.

An AI-powered Quality Assurance (QA) tool developed to enhance the customer support experience by automatically analysing customer-agent conversations( both actual human agents as well as agentic bots too). It identifies policy violations, customer sentiment, and agent performance insights in real-time. Platform integrates with CRMs and uses machine learning (ML) and natural language processing (NLP) to offer actionable insights for businesses seeking to improve service quality and operational efficiency. This will help organisations to streamline support analysis, improve agent training, and monitor customer satisfaction metrics.