Project 1: AI Chatbot
The projects below explore different applications, ranging from deployed systems to product builds and applied evaluation.
Designed, architected, and implemented an AI chatbot to improve how users access and interact with organizational knowledge.
The work spanned system architecture, interaction flows, and interface design, implemented using a Python-based backend, retrieval-augmented workflows, and a lightweight web UI to deliver a streamlined, reliable user experience.
Project 2: Job Seeking Chrome Extension
Designed and built a custom Chrome extension to analyze LinkedIn job postings in real time.
The extension uses JavaScript-based pattern recognition to parse job listings and extract key information such as visa sponsorship indicators, compensation ranges, and location or work modality (remote, hybrid, or on-site).
In addition to metadata extraction, the system analyzes job descriptions to support resume tailoring by identifying relevant skills and experience signals for each role. This reduces the need for manual review and repetitive resume customization during the application process.
The extension integrates directly into LinkedIn’s interface and surfaces insights through a lightweight popup panel. It is built with vanilla JavaScript and modular CSS, operates entirely client-side, and avoids external dependencies to ensure fast, reliable performance.
Project 3: AI Tools Suite
This project explores how AI can be applied as a practical operating layer for early-stage founders managing marketing, content, and growth workflows. I designed and built a modular AI tools suite that integrates multiple task-specific agents into a single, cohesive product experience, focused on speed, clarity, and decision support rather than automation for its own sake.
The work spans product design, system architecture, and end-to-end implementation, with an emphasis on building tools that slot naturally into real founder workflows.
What I Built
I designed and implemented an AI-powered tools suite composed of multiple focused modules, each addressing a distinct marketing need:
- AI Assistant Conversational interface for contextual queries, planning, and execution support across marketing tasks.
- Trend Intelligence Real-time aggregation and analysis of trending content across platforms (X, Reddit, TikTok, Instagram, YouTube), surfaced as actionable insights rather than raw data.
- Content Studio A Google Sheets–native workflow for generating brand strategies, content calendars, audits, and copy—built as a custom Apps Script extension with AI-backed actions.
- MCP Platform (Model Context Protocol Demo) A demonstration of structured AI orchestration, showing how shared context can be passed across tools to maintain continuity between analysis, generation, and execution.
- AI Sales Copilot (Prototype) Exploratory module focused on outbound and prospecting workflows across Slack, email, and social platforms.
Project 4: Human-Centered AI Analysis — Bumble Safety Features
This analysis mapped Bumble’s Private Detector and Deception Detector to established Human-Centered AI (HCAI) principles to understand how AI-driven safety features are translated into trustworthy, user-facing product experiences.
Human-in-the-Loop / Control: AI surfaces risk signals but does not take action. Users retain full control over how to respond.
Augmentation over Replacement: Safety features enhance user awareness rather than making social decisions on the user’s behalf.
Transparency & Explainability: Interventions are framed as indicators, not definitive judgments, supporting informed decision-making.
Fairness & Non-Discrimination: Detections focus on behavioral patterns, avoiding identity-based labeling or stigmatization.
Privacy & Security: Analysis occurs within private conversations without exposing or persisting sensitive content.
User-Centered Design & Empathy: UX language emphasizes support and safety, not enforcement or alarm.
Accountability: Clear user actions (report, seek support) are provided when interventions occur.
