🏗️ BafnaTrack: Enterprise ERP

Engineered & Architected exclusively for DBafna Developers.

BafnaTrack is a high-performance Real Estate ERP (Enterprise Resource Planning) system. It serves as the digital "Central Nervous System" for DBafna Developers, replacing fragmented spreadsheet workflows with a logic-driven, relational cloud ecosystem.

---

📸 The System Interface

📱 Mobile Field Unit

Engineered for Sales Agents to execute complex financial modeling on-site.

1. General Interface & Lifecycle

Navigation, Administration, and Project History.

A. Operations Dashboard	B. Admin & Settings	C. The Archives
Global Stats & Projects	Admin Management Space	Project History

2. Unit Analytics (View Mode)

Comprehensive breakdown of unit details. (Showing full scroll view).

A. Unit Details (Top)	B. Unit Details (Bottom)
Basic Info & Status	Customer Details

3. The Financial Logic Engine (Edit Mode)

The complex form where financial logic, loans, and calculations happen.

A. Configuration (Top)	B. Financial Math (Mid)	C. Finalization (End)
Pricing & Parking Logic	Loan & Bank Due Logic	Notes & Uploads

💻 The Executive Command Center (Desktop)

A high-level dashboard for data aggregation and strategic decision making.

📊 Master Operations Dashboard

Visualizes Total Asset Value (TAV), Sales Velocity, and Inventory Health across all sites in real-time with opened hamburger menu.

🎛️ The Admin Control Panel

The central configuration hub. This is where Admins execute "God-Level" commands: Creating new projects, running the Bulk-Generation algorithm, and managing Archives.

---

🧠 Engineering The "Logic Core"

Unlike standard apps that simply store data, BafnaTrack actively computes it. I designed three core algorithmic engines to power the business:

1. The Financial Computation Engine 💸

The Problem: Manual loan calculations were error-prone due to varying "Project Completion Percentages" affecting bank disbursements. My Algorithm: I implemented a reactive financial model. The moment an admin toggles "Has Loan":

1. The system queries the global Project_Completion_Rate.
2. It dynamically computes: Bank_Liability = Loan_Amount * (Completion_Rate / 100).
3. It instantly derives the Customer_Payable_Balance. Result: Eliminated financial calculation errors by 100%.

2. The Bulk-Generation Algorithm ⚡

The Problem: Manually creating database entries for a 100-flat building took hours. My Algorithm: I wrote a custom Dart loop injection script.

• Input: Building Name (e.g., "Orchid"), Wing Count, Floors, Units per Floor.
• Process: The system iterates through the parameters, generating unique IDs (A101, A102... B101...) and pushing a batched transaction to Supabase. Result: Reduced data entry time from 4 hours to 4 seconds.

3. Relational Cascade Integrity 🔗

The Problem: Deleting a project often left "orphan" data (stray flats or files) in the database. My Algorithm: I architected a Cascade Delete Protocol. If a Project is deleted by an Admin, the system recursively identifies and purges:

• All associated Flat records in PostgreSQL.
• All linked Quotation images in Supabase Storage buckets. Result: Maintains a pristine, zero-waste database environment.

---

🛠️ Technical Architecture

• Core Framework: Flutter (Dart)
  • Role: Provides the "Pixel-Perfect" rendering engine required for the complex financial grids.
• Backend Infrastructure: Supabase
  • Role: Replaces traditional APIs with a direct-to-database connection.
• Database Topology: PostgreSQL
  • Role: Handles complex relational mapping (Project 1:N Flats 1:N Documents).
• State Management: Reactive Streams
  • Role: Ensures that if a flat is sold on Mobile, the Desktop dashboard updates instantly.

---

👨‍💻 Internship Impact

Developed independently at DBafna Developers, this system dismantled the operational complexity of manual tracking. It stands as a testament to my ability to engineer logic-heavy, business-critical software that solves tangible real-world problems.

Architected & Developed by Payal Dharma Mehta.