As the founder of Peekly, built an AI-driven marketing analytics platform from the ground up. My vision was to enable a truly conversational "talk to your data" experience, empowering businesses to get instant insights without complex dashboards.

Designed and built a unified, multi-agent LangGraph pipeline to integrate and analyze siloed data sources like web traffic, ads, e-commerce, and payments. This approach slashed the time it takes to get actionable insights from approximately two hours to under 30 seconds.

Crafted seamless API integrations with platforms like Google Analytics, Shopify, and Stripe, which automated data synchronization and reduced client onboarding time from days to just minutes.

Top Impactor 2024

Spot awards 2024, 2023 and 2022

Transforming Businesses with AI & Data

Worked as an accomplished leader and builder, specializing in leveraging advanced data analytics and AI to create strategic solutions that drive business transformation. Led diverse, cross-functional teams to develop innovative systems that boost efficiency and profitability, from demand forecasting to root cause analysis.

At Converge Consumer, I led a 12-member team to deliver Demand Brain, a custom-featured forecasting engine that boosted accuracy by 15%, directly optimizing marketing, pricing, and promotional activities. I engineered a PySpark pipeline and a Feature Factory using Kubeflow and Feast to automate feature engineering, slashing analysis time by over 90% and development time by 75%.

As part of the Smart Factory initiative, I developed a chat-based Generative AI multi-agent system for root cause analysis in manufacturing. This system, using a RAG framework, diagnosed faults by analyzing sensor data and manuals via a knowledge graph, accelerating identification by 80% and automating ServiceNow ticketing. Through A/B testing, DeepEval, and Pairwise Preference Testing, I improved the LLM's response approval rate by 42%.

During my work with Google, I built Cymbal Labs, a cloud-based enterprise search solution using LangChain, Vertex AI, and Search API, showcased at Google Cloud Next ’23. I optimized the system’s performance by reducing latency from 6.0s to 2.2s and introduced a converse mode with Redis-backed session memory for persistent, multi-turn interactions.

My passion is turning complex data and AI challenges into tangible business advantages, focusing on technical excellence that delivers measurable impact.

Specialized AI & NLP Solutions

As an accomplished developer and builder with a passion for using Natural Language Processing (NLP) and AI to solve complex, domain-specific challenges. Worked on solutions that transform how information is accessed, analyzed, and understood in specialized fields like legal and medical research.

Key Projects

PubMed Search: I developed a specialized NoSQL data store solution on DynamoDB to enable context-based, zero-shot search across vast amounts of medical research data. This system leveraged AWS Comprehend's Named Entity Recognition (NER) to intelligently parse and organize gigabytes of information, making it more accessible and useful for researchers.

Legalisis: I architected a contract comparison system that cut down legal review time from hours to minutes. This solution used Sentence-BERT for semantic similarity and SpaCy NER for key entity extraction. I also developed an abstractive summarization model and a one-shot BERT model to create a comprehensive legal document analysis platform. This platform provides rapid insights and identifies critical terms and conditions, streamlining the legal review process.