Bengaluru engineer creates AI-powered app that detects potholes and tracks responsible contractors

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Gaurav Sen built an AI tool that combines dashcam footage, GPS and accelerometer data to detect potholes on Bengaluru roads. The system searches through 2,900 government contracts to identify contractors responsible for damaged road stretches and generates complaint records within four seconds.

Bengaluru Engineer Transforms Daily Commute Frustration Into AI Solution

Gaurav Sen, a Bengaluru engineer, has developed an AI-powered app that addresses one of India's most persistent civic infrastructure issues: potholes. The system combines hardware and AI vision models to detect damaged roads during regular drives, then identifies which contractor should repair them by cross-referencing government contracts

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. Sen demonstrated the technology on August 16, 2026, through a video shared on Instagram in collaboration with ChatGPT India, which later gained attention on social media platforms

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How the AI Tool to Spot Potholes Works

The pothole detection system relies on three hardware components installed in Sen's vehicle: a dashcam, GPS and accelerometer. As he drives through Bengaluru streets, these devices continuously collect road-related data. The recorded footage is then processed using AI vision models that can identify and classify potholes according to size—small, medium or large

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. Sen built the application using Codex and connected it with OpenAI for processing the collected information

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. The technology also tackles a common challenge on Indian roads: distinguishing potholes from speed breakers when viewed from a distance.

System Identifies Responsible Contractor Through Government Contracts

What sets this AI-powered app apart is its ability to connect each detected pothole with accountability. The system searches through 2,900 government contracts to identify the contractor responsible for the specific road stretch where damage has been found

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. This feature proves especially useful for roads still under warranty, where contractors remain legally obligated to fix defects without additional government expenditure. The app compiles a complete record within approximately four seconds, including the pothole photograph, exact location via GPS, relevant tender numbers and the officer responsible for that road section

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Test Drive Reveals Road Maintenance Accountability Gaps

During a test drive to work, Sen's system detected 12 potholes along his regular route, generating photographic and location-based evidence for each one

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. The experiment demonstrated how AI can reduce the effort required to document civic infrastructure issues. Each detected pothole resulted in a ready-to-file complaint that could be submitted to civic authorities or contractors with all necessary documentation already prepared. This approach transforms a routine commute into a systematic data collection exercise that builds evidence for action on damaged roads.

Social Media Response and Future Implications

The project gained widespread attention after reaching X, where users praised the practical application of AI to everyday civic problems affecting motorists across Indian cities. One social media reaction described the initiative as "Fixing real-life bugs," while another user wrote, "Great idea and execution"

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. Some users suggested that civic agencies could adopt similar technology to identify damaged roads and respond to complaints more quickly. The system shows how combining everyday hardware like dashcam and accelerometer with AI vision models can generate actionable civic data. Watch for potential adoption by municipal authorities seeking to improve road maintenance accountability and whether this approach spreads to other cities facing similar infrastructure challenges.

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