AI & Technology

Turn ambiguous goals into agentic systems: orchestrate LLMs, data pipelines, and human-in-the-loop flows that reduce manual effort and improve decisions.

Current Focus

ASC (Agentic Systems for Commerce)

Building agentic commerce intelligence pipelines that turn retail signals into actionable decisions and measurable outcomes.

OakFire.ai

Contributing to an early-stage SaaS platform that helps users clean datasets and generate insights.

LangGraph Extensions

Supporting the team with internal tooling to facilitate multi-model orchestration.

Areas of Contribution

Agentic workflows

LLM orchestration

E-commerce analytics

Data engineering strategy

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ASC
Commerce Systems
LLM
Orchestration
Agentic
Workflows

Professional Builds

Contributing to enterprise systems that help teams achieve measurable outcomes through collaborative automation and AI solutions.

E-commerce PDP Monitor

ASC Trackers • Python, Automated Monitoring

Contributed to building an automated monitoring system with the team that tracks product optimization rollouts across 83 high-priority ASINs. Features intelligent change detection, multi-time window analysis, and executive-ready HTML reports.

PythonWeb ScrapingAutomation

Team Impact: 2.5 hours/week saved across workflows

AI Content Generation Pipeline

Brief Generation • OpenAI API, AsyncIO, FastAPI

Collaborated on architecting a multi-agent AI system for automated product optimization brief generation. Features parallel processing, structured JSON outputs, automated fact-checking, and cloud storage integration.

OpenAI APIAsyncIOAWS S3

Team Impact: Helped reduce brief creation time by 80%

ROI Impact Calculator

ASC KPI • Next.js 15, TypeScript, PostgreSQL

Helped develop a full-stack analytics application with the team that measures business impact of e-commerce optimizations. Features real-time dashboards, ML-powered scoring, and statistical confidence analysis.

Next.jsTypeScriptRecharts

Team Impact: Enabled data-driven decision making

Analytics Data Pipeline

McKinsey Dataset • Python, XGBoost, PostgreSQL

Contributed to creating a robust data pipeline processing ~400 ASINs with ~2000 search terms. Features ML-based sales interpolation, intelligent caching for 4x performance improvement, and comprehensive validation.

XGBoostData EngineeringCaching

Team Impact: Contributed to 4x processing speed improvement

These collaborative projects reflect our team's commitment to thoughtful AI integration, scalable data engineering, and creating shared value through intelligent automation.