Nozzle firmware
C++ firmware on the ESP32 that counts flow meter pulses, reads the dispenser signal, converts both to litres and latches a tamper alert the moment the nozzle sensor trips.
A fuel monitoring system that checks every fill at the nozzle, with ESP32 firmware, a Flutter app, a FastAPI backend and an admin panel for stations.
( The challenge )
When drivers, families and fleet owners fill up, they see one number: the reading on the pump. They have no way to check that the fuel that went into the tank matches it. A small shortfall on every fill adds up quickly, and without a record nobody can show where it happened.
Stations needed the same answer from the other side: a way to prove honest fills, find the dispensers and staff behind repeated problems, and act on it, all from one place.
( Our approach )
We put an independent measurement at the nozzle itself. An ESP32 device fitted there counts the fuel with its own flow meter and reads the dispenser's pulse signal at the same time, so every fill is measured twice and compared litre by litre.
The device runs its own WiFi hotspot, so the customer's phone connects to it directly and the check works at any pump, with or without mobile signal. The app shows the live readings every second, captures photo evidence and syncs each transaction to the backend, where configurable rules raise fraud alerts and escalate repeat cases automatically.
Stations manage everything from a web admin panel: a live view of every nozzle, fraud alerts, devices, transactions, reports and staff, with firmware updates sent to the devices over the air.
( What we built )
Delivered in 6 months, with the source code, accounts and documentation handed over in full.
C++ firmware on the ESP32 that counts flow meter pulses, reads the dispenser signal, converts both to litres and latches a tamper alert the moment the nozzle sensor trips.
A Flutter app that scans the pump's QR code, connects to the device's hotspot and shows both readings side by side every second until the fill is complete.
Every transaction is saved on the phone first and synced the moment a connection returns, so a fill recorded without signal is never lost.
A FastAPI backend that checks each fill against configurable discrepancy rules, raises fraud alerts and escalates staff and stations with repeated flags to a blacklist.
A Next.js panel with a live nozzle monitor, fraud alerts, device management, transactions, reports, staff and complaints, with access set by role.
Devices are registered from the panel with their own key, receive commands remotely and install firmware updates over the air, reporting progress as they go.
( The outcome )
( How it was delivered )
Sign in, the app and API foundations, and the live session between the phone and the nozzle device.
The admin panel modules for stations, with transaction history and reports.
Production deployment of the backend, and features for fleet owners managing vehicles and drivers.
Flagging and blacklisting rules, the remote command system for devices and over the air firmware updates.
( Services used )
( Built with )
Mainstream tools with deep talent pools, so the client is never dependent on us to maintain what we built.
( Read next / Education )
The founder reported PKR 1M+ in revenue in the first two months after launch, with no ad spend. The platform: an AI writing checker, speaking scores, Easypaisa plans and an admin panel, built in 2 months.
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