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AI-Powered Retail Operations & Management Platform
I designed and developed a full-stack retail operations platform that centralized the day-to-day processes of a real operating business into one connected system.
The platform brought together POS and sales processing, inventory and shelf management, stock transfers, customer management, promotions, loyalty rewards, employee shifts and alerts, online orders, analytics, and reporting within a single web application.
I built the solution end-to-end — translating real business requirements into features, designing the system architecture and database, developing the frontend and backend, implementing business logic and APIs, and handling testing and deployment.
I also incorporated AI-assisted capabilities and automation into the wider platform to reduce repetitive operational work, surface useful business information, and support faster decision-making.
The result replaced fragmented workflows with a centralized digital system, giving the business better visibility over sales, inventory, employees, customers, and day-to-day operations, while creating the technical foundation for its e-commerce operations.
The network for creativity
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You nailed the overall mood here. The details just reinforce it nicely.
Built Gloss Instant Booking for the #lovablechallenge.
I created a booking experience for a fictional solo automobile detailing business in Austin where the owner was losing time to back-and-forth booking messages.
Instead of:
Inquiry → questions → calendar checking → confirmation → reminder
Gloss turns it into:
Inquiry → availability → confirmed booking → automated follow-up.
Built with @Lovable.
#lovablechallenge
Really like how clearly you've framed the before vs. after workflow here. For a mobile detailing business, did you also account for travel time/location when determining availability?
Feels like that's where scheduling gets especially interesting once bookings start stacking up.
An experimental AI-agent application for researching business opportunities using web search, webpage extraction, evidence collection and structured analysis.
The evidence collection step is where I would look first. When two pages disagree, do you keep both source links so someone can see why the analysis chose one?