Work.

Multimodal GenAI Compliance System

• Architectural design images were reviewed manually against internal policy standards. Slow, inconsistent, and difficult to scale across a large regulatory body.
• Built a multimodal GenAI pipeline where users upload design images and vision agents automatically check them against policy rules. Output is a structured compliance report across three categories: Compliant, Non-Compliant with detailed reasoning, and Needs Review.
• Integrated MinerU, a non-OpenAI-compatible model, by building a custom FastAPI wrapper and deploying it on the Katonic platform. Full agent architecture built using Groq and custom-deployed models.
• End-to-end ownership across architecture, development, customer stakeholder management, and internal product alignment.

GenAI-Enabled QA Automation Engine

• A 60-member QA team spent significant time and cost on testing despite heavy automation investment. The end-to-end QA lifecycle was still largely manual.
• Built a GenAI engine that automated the full QA lifecycle: from user story to test case generation, and from test cases to executable test scripts. Removed the most repetitive steps from the process entirely.
• 33% productivity gains across end-to-end QA test cycle execution, freeing the team for higher-value work.

Private LLM-Powered Customer Support Chatbot

• Customer support queries required searching across large volumes of technical documentation, with resolution times running into days.
• Built a conversational AI system using Advanced RAG, with open-source LLMs identified and deployed privately on AWS EC2 to meet strict data security requirements.
• Query resolution time reduced from days to minutes. Response accuracy held above 95% in production.
• Also contributed NLP pipelines for named-entity extraction, Flask REST APIs for full-stack integration, and database design across the solution.

Text-to-SQL for Leadership Analytics

• Leadership depended on analysts to pull reports from complex databases, creating delays and a constant bottleneck on the data team.
• Built a Text-to-SQL system using a RAG pipeline where users ask in natural language and get SQL-backed results with automated dashboard output. A Schema Intelligence Agent maps natural language intent to the right tables, columns, and relationships. A Dashboard Generator Agent converts query results into visual reports for leadership.
• Reduced dashboard access time and removed analyst dependency for routine reporting.

AI-Driven Mental Health Platform

• Access to structured, consistent mental health support is limited, especially in schools and clinical settings where professional bandwidth is a real constraint.
• Built a CBT-based platform providing AI-driven therapy conversations and patient assessments. Deployed and used across schools in North India and piloted at a major hospital in Gurugram.
• Led product and UI development using React JS, working directly with clinical practitioners to align the experience with therapeutic frameworks.

D2C E-Commerce Intelligence Platform

• D2C brands had no reliable way to track competitor pricing and market trends at scale without heavy manual effort.
• Built a scalable backend for price tracking, historical analysis, and trend prediction across multiple e-commerce platforms.
• Core component is a multi-agent web scraper for large-scale data collection, handling dynamic content, rate limiting, and data validation automatically. Reduced data processing time by 60%.

POC Lab

A selection from a wider body of experimentation spanning telecom, enterprise software modernization, and multi-agent AI systems.

• Log Lifecycle Automation (Telecom). Automated the full lifecycle of log management for customer support engineers at a major telecom client. Goal was to reduce resolution time from days to under 2 hours by intelligently triaging and routing log events.
• Java to Microservices Migration (CrewAI). Agentic solution that analyzes legacy Java monolith codebases and generates microservice architecture recommendations, reducing manual effort in large-scale modernization work.
• Multi-Agent E-Commerce Scraper. Adaptive scraping framework with domain-specific agents capable of handling a wide variety of site structures reliably at scale.
• RAG Pipelines with Multi-Agent Orchestration. Multiple RAG implementations across domains, with orchestration layers managing retrieval, reranking, and response synthesis across specialized agents.