I built The Quiet Chair, a booking experience for a fictional single-chair hair studio in Baner, Pune, run by a solo stylist named Mira.

Mira’s problem is simple: every booking message interrupts an appointment. Checking availability, confirming times, and handling changes take attention away from the customer in her chair.

The app lets customers choose a service, see available times, and receive an immediate booking confirmation. Availability accounts for opening hours, lunch breaks, existing appointments, and cleanup time. Customers can request a quiet appointment and use a secure link to reschedule or cancel, with online changes closing four hours before their appointment.

Mira has a protected owner workspace to view her daily agenda, check customer preferences, manage appointments, and block personal time.

I built it to turn “Can I book with you?” into “You’re booked” without needing a reply from the owner—and to make later changes just as straightforward. The result is fewer messages, fewer manual calendar checks, and more time for Mira to focus on her clients. @Lovable

Agent Evaluation Dashboard — Full-Stack LLM Observability Tool

Teams running AI agents in production need visibility into how those agents actually perform not just whether they respond, but how accurate, fast, and cost-efficient they are. I built a full-stack observability dashboard to solve exactly that: a single place to track accuracy, latency, API cost, and schema compliance across multiple LLM models and agent types.

What I built

A responsive Next.js (App Router) frontend styled with Tailwind CSS — deep purple sidebar, live accuracy/latency charts (Recharts), filterable eval-run tables, and per-model performance comparisons

A Python FastAPI backend serving the dashboard's data layer, with token-based authentication protecting every route

Full session handling: login, protected pages, automatic redirect for unauthenticated users

A fully responsive layout collapsible drawer sidebar on mobile, reflowing grid on tablet/desktop