Unify customer conversations, automate responses, and help teams respond smarter with AI.
Project description
InboxPulse is an AI-powered omnichannel customer communication platform designed to bring conversations from multiple channels into one intelligent workspace.
I worked across product strategy, workflow design, AI integration, and product engineering to create a system where teams can manage customer conversations, collaborate efficiently, and use AI to improve response workflows.
The platform combines a unified inbox, AI-powered response assistance, team collaboration, customer management, and communication analytics, helping businesses reduce fragmented workflows and manage customer interactions more efficiently.
Key capabilities
Unified Inbox - manage customer conversations from multiple channels in one place.
AI-Powered Support - generate contextual AI assistance for customer communication.
Omnichannel Communication - connect messaging and website communication channels into a centralized workflow.
Team Collaboration - assign and manage conversations across customer-facing teams.
Customer Management - organize customer information and conversation history.
Real-Time Insights - understand communication activity and team performance through analytics.
A: AI-first. No tabs, no dashboards. The app is one conversation with a coach that already knows you slept six hours and squatted heavy on Tuesday. You log sets by talking. The plan rewrites itself mid-workout.
B: The modern standard. Home, today's workout, set-by-set logging, class booking, progress. Everything a member expects, done properly.
Same brand, same components, same build quality. The only real difference is how much you're asking the user to unlearn.
Going with A here, the idea of the plan rewriting itself mid-workout instead of you scrolling a dashboard feels like where fitness apps eventually land. Would you actually ship the AI-first version to real users right now, or is it more of a thought experiment at this stage?
I analyzed 3M+ job postings across 161 countries and 93K companies to identify hiring trends, in-demand roles, work-mode patterns, and salary insights.
I used Python for data cleaning and preprocessing, SQL for analysis, and Power BI to build an interactive dashboard.
Key areas analyzed:
• Global hiring demand
• Top job roles by demand
• Remote vs. onsite/hybrid work patterns
• Average salary trends
• Monthly hiring trends
This project helped me turn a large dataset into clear, actionable insights for understanding the global job market.
Created an AI-powered lead generation and outreach tool for finding and converting local business leads.
The system scrapes Google Maps, analyzes business websites with Gemini AI, and generates personalized pitch emails automatically.
I built the complete pipeline from scraping to AI analysis and outreach.