A self-initiated product: a booking and ordering system for a billiard club in Tbilisi. I designed it in Figma from scratch and built all of it, from the interface to the database. Guests call the club and nobody answers. They arrive and every table is taken. They want food and wait at the bar. Side Pocket moves all of that to the website. Key decisions No guest accounts. A booking needs a name and a phone number. The guest gets a private link to move, extend or cancel. The link is the account. A 15-minute hold. Guests check in with "I'm at the table." A table nobody claims goes to the waitlist, and the next person gets the same 15 minutes. Ordering tied to the table. Guests order from their phone during the game, add time themselves and see the bill as it grows. One screen for staff. Every table's state for the night, late arrivals and orders to serve pinned to the top, bookings and orders boards, alerts in Telegram. A Telegram bot. It understands "table for 4 tonight at 8" in English or Russian and finishes the booking with buttons. Scope Guest site with live availability, single and group booking, waitlist, private booking page, table page with menu, cart and bill, email confirmations and reminders, staff dashboard, Telegram bot. Stack React 19, TanStack Start, TanStack Router and Query, Tailwind 4, Radix UI, Supabase Postgres with row-level security, pg_cron, Brevo and Resend, Telegram Bot API, Vitest. Business rules sit in plain functions, separate from the UI and the database, with 73 tests. Only server functions touch the database. My role Product design, UI design, front-end, back-end, database, integrations. Live demo: side-pocket.lovable.app

Built for the #LovableChallenge by @Contra and @Lovable. #BuiltWithLovable😊

nicety accomplished.. great work

Messy Data In. Clearer Decisions Out. 📊

Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.

That's the gap I work in:

→ Cleaning and analyze data with Python & R

→ Turning findings into investor-ready pitch decks

→ Polishing the final write-up so nothing gets lost in translation

If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.

Open to projects. Let's talk 👇