Bengaluru: A Bengaluru-based software engineer and entrepreneur has demonstrated how artificial intelligence can be used to tackle one of India’s most persistent civic problems — potholes.
Gaurav Sen, founder of Interview Ready, recently showcased an AI-powered system capable of detecting potholes while a car is being driven, identifying their locations, analysing their size and generating structured civic complaints.
The project has attracted attention because it attempts to go beyond simply identifying damaged roads. The system can reportedly connect a detected pothole with government contract records, identify the contractor responsible for the road and locate the relevant government official.
How the AI pothole detection system works
The system combines a dashcam with GPS and an accelerometer to collect information while a vehicle travels along a road.
The camera captures the road ahead, while GPS records the location of potential potholes. The accelerometer can provide additional information about the movement of the vehicle as it passes over uneven surfaces.
AI-based computer vision is then used to identify potholes and assess their size. The system also attempts to differentiate potholes from other road features, such as speed breakers.
This means the technology is designed to operate during an ordinary drive rather than requiring a separate road inspection.
Once a pothole is identified, its geographical coordinates and photographic evidence can be recorded automatically.
AI connects potholes with government contracts
The more significant part of the project comes after a pothole has been detected.
According to the demonstration shared online, the system can search through approximately 2,900 government contracts to identify the tender associated with the road where the pothole was found.
It can then determine the contractor responsible for the relevant road work and identify the concerned government officer.
The system can combine this information with the pothole’s photograph and geographical coordinates to generate a structured complaint.
In one example demonstrated by Sen, a single drive detected 12 potholes, following which the system generated 12 complaints that were ready to be filed.
Such automation could potentially reduce the amount of manual effort required from citizens who want to report damaged roads.
Bengaluru already has pothole reporting systems
The concept is particularly relevant to Bengaluru, where citizens have long used civic platforms to report potholes and other infrastructure problems.
The former Bruhat Bengaluru Mahanagara Palike’s ‘Fix My Street’ system allowed residents to submit geo-tagged photographs of potholes and direct complaints to the concerned ward officials.
The current Greater Bengaluru Authority website also lists a ‘Fix Pothole’ service among its civic applications.
Government documents indicate that road construction and maintenance works are regularly carried out through contracts, including provisions related to maintenance and defect-liability periods.
The AI system demonstrated by Sen attempts to bring these separate elements together — road detection, location tracking, government records and complaint generation.
From detecting problems to assigning accountability
Traditional pothole reporting generally requires a citizen to notice a damaged road, take a photograph, record the location and submit a complaint through an appropriate civic platform.
The proposed AI system seeks to automate much of that process.
A camera detects the problem, GPS identifies where it is located and AI analyses the image. Government records can then provide information about the road contract and the parties responsible for maintenance.
The final step is generating a complaint containing the available evidence.
This approach could potentially shift pothole reporting from individual complaints towards continuous, data-driven monitoring of road conditions.
However, the effectiveness of such a system would ultimately depend on the accuracy of pothole detection, the availability and quality of government contract data and whether the concerned authorities act on the complaints generated.
Why the project has attracted attention
The demonstration has generated considerable discussion online, particularly among younger users interested in practical applications of artificial intelligence.
Much of the public conversation around AI has focused on chatbots, image generation and content creation. Sen’s project presents a different use case by applying AI to a physical, everyday civic problem.
The system also raises a broader question: if artificial intelligence can identify potholes and connect them with public records, could similar technology be used to identify other infrastructure problems?
Potential applications could include damaged streetlights, overflowing garbage points, broken footpaths, water leaks and other visible civic issues.
Such systems could potentially help authorities receive structured information about problems across large urban areas without relying entirely on individual residents to manually submit complaints.
Could the model work in other cities?
The idea has also prompted discussions about whether similar technology could be adapted for other Indian cities.
Potholes and road maintenance remain recurring concerns in urban areas across the country. A system capable of automatically collecting evidence and linking road damage with maintenance records could potentially offer civic authorities another tool for monitoring infrastructure.
At the same time, deploying such technology at scale would require reliable government databases and clearly accessible information about road contracts, maintenance responsibilities and complaint-handling mechanisms.
The technology would also need to minimise false detections and ensure that complaints are not generated repeatedly for the same road damage.
Gaurav Sen’s technology background
Sen’s professional background is also rooted in the technology sector. His LinkedIn profile identifies him as the founder of InterviewReady and lists previous experience as a software engineer at Uber, a platform engineer at Directi and an IT analyst at Morgan Stanley.
His profile also mentions an entrepreneurship accelerator programme at Cornell University and a bachelor’s degree in computer science from Fr Conceicao Rodrigues College of Engineering.
The pothole project therefore represents an extension of his technology and entrepreneurship background into a practical urban problem.
A practical example of AI beyond chatbots
The Bengaluru pothole project illustrates a broader possibility for artificial intelligence.
Instead of simply answering questions or generating content, AI can be combined with cameras, location data and public records to identify real-world problems and turn them into actionable information.
In this case, the process begins with a camera observing a road. AI identifies a pothole, GPS establishes its location, government records provide information about the road and its maintenance contract, and software prepares a complaint.
For Bengaluru residents who encounter damaged roads during their daily commute, the concept could represent a more automated route from identifying a problem to seeking accountability.
Whether such a system can be expanded into a reliable city-wide solution will depend on data access, technical accuracy and, most importantly, the response of civic authorities. But the demonstration offers an example of how AI could move beyond conventional digital applications and become a practical tool for addressing everyday public infrastructure issues.