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Venture Vault Capital - Base44 to Supabase Migration
Migrated a funding platform from Base44 to Supabase, modernizing its backend architecture while preserving existing application functionality. The project covered PostgreSQL database migration, Supabase Authentication, data modeling, access control/RLS, API integration, codebase updates, testing, and deployment validation.
Key Highlights:
✔ Base44 → Supabase/PostgreSQL migration
✔ Supabase Auth & user management
✔ Database schema and data integrity migration
✔ Row Level Security (RLS) and permissions
✔ Backend/API compatibility updates
✔ Testing, deployment, and migration documentation
Stack: Supabase, PostgreSQL, GitHub, REST APIs, Authentication, RLS
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This UI was really great nice job Piotr🔥🔥🔥
CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System
An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.
Overview
CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions.
The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.
The Problem:
Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly.
CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.
My Solution:
I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.
The platform includes:
Real-time people detection and counting.
Crowd occupancy estimation.
Crowd-density classification.
Entry and exit line counting.
Zone-based monitoring.
Incident and anomaly monitoring workflows.
Camera health and tamper monitoring.
Evidence management and reporting.
Telegram alert notifications.
Remote edge camera processing.
Live monitoring dashboard.
Technical Implementation:
Machine Learning & Computer Vision
Python
YOLO / Ultralytics
OpenCV
NumPy
Pandas
Backend:
Python
FastAPI
REST API
Frontend:
React
Vite
JavaScript
Other Technologies
Git
GitHub
Telegram Bot API
JSON/CSV data storage
My Role
Founder & Full-Stack Machine Learning Developer
For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?
Trax Omni: Multi-Company Sales CRM + Mobile App, Built From Scratch
THE PROBLEM
Small and growing sales teams run on WhatsApp chats, Instagram DMs, spreadsheets and memory. Leads come from five places, nobody knows who replied to whom, follow-ups are forgotten, and the owner can't see what's actually in the pipeline. Every slow reply is a lost customer.
WHAT WE BUILT
Trax Omni, a complete sales CRM product, designed and built from scratch by UI Trax:
Product website: explains the product, shows industry templates, and lets a business create its own workspace
Today dashboard: total leads, open pipeline, won revenue, win rate and the next actions to take
Leads: source, stage, value, owner and score for every inquiry, with import and filters
Pipeline: drag-and-drop stages from Inquiry to Won, with custom stages
Sales Copilot: ask in plain words ("show my hot leads with no activity for 3 days"); it finds leads and drafts follow-ups, and nothing changes until you confirm
Tasks & follow-ups: open, overdue and completed tasks, so no lead goes cold
Automations: round-robin lead assignment, follow-up tasks and stage-based next steps
Reports & forecast: pipeline distribution, weighted forecast and revenue targets
Team & roles: separate owner, manager and employee views; each person sees only what they should
Campaigns: Meta and TikTok lead campaigns, with their leads brought into the pipeline
Quotes & invoices, WhatsApp inbox and email connected to each lead
Mobile app for iOS & Android with a full English and Urdu interface
HOW IT WAS BUILT
Multi-company setup: each business gets its own private workspace
Backend in Node.js with a PostgreSQL database
Mobile app in React Native (Expo)
Secure sign-in with Google Workspace / Microsoft SSO and MFA
Automated test suites for the backend, the mobile app and the dashboards
STATUS
Built and in final launch testing. The screens show sample data from the interactive preview.
