Editorial note: This story has been written using secondary information from published reports and publicly accessible information about Bribes.fyi. The creator was not interviewed directly for this piece.
A bribe is usually designed to leave no trace. A cash payment changes hands, a demand is made quietly, and the person walking out of a government office often has little more than a frustrating story to tell.
But what if thousands of those stories could be turned into data?
That is the idea behind Bribes.fyi, a crowdsourced platform created by Aryan Nishad, a B.Tech student at Maharaja Agrasen Institute of Technology in Delhi. The platform lets people anonymously report alleged bribe demands and then uses those reports to show patterns across cities, states and government departments.
Turning everyday experiences into data
Bribes.fyi was launched on 2 July with a simple question at its core: what if there was public data on the everyday corruption people encounter while accessing government services?
In about a minute, users can anonymously report where a bribe was demanded, how much was asked, and whether they paid or refused. Photograph: (AI generated)
Nishad describes the platform as a privacy-first space where people can report bribery incidents without creating an account. A user can enter details such as the government department involved, the city, the amount allegedly demanded, the purpose of the payment, and whether they eventually paid or refused.
The process takes about a minute. Once submitted, the report appears anonymously, with identifying information removed, and becomes part of a searchable database.
The idea is to move beyond individual complaints. One person’s experience may be easy to dismiss as an isolated incident. Hundreds of similar reports from the same department or city can begin to reveal a pattern.
A map of reported bribes
The platform organises submissions into categories such as Reports, Departments and Cities, allowing users to browse and compare reported incidents.
It also features a map showing where reports have come from across India. Departments currently listed include the Police, RTO, Revenue and Land Records, Passport Office, Municipal Corporation, GST, Electricity Board, Income Tax, and Food and Drug Administration.
From police stations to RTOs, the platform maps reported bribe demands across cities, states and government departments. Photograph: (Instagram/@dailybrewindia)
As of 17 August, the website showed hundreds of reports across more than 250 cities, with police departments accounting for the highest number of submissions. The platform also tracks whether people refused to pay. At the time, more than a third of reported incidents were marked as refusals, with some users saying they still received the service they needed.
The numbers, however, come with an important caveat: these are self-reported experiences, not an official measure of corruption. The platform itself says its data should not be treated as a definitive picture of bribery across India.
Built with AI, but designed for citizens
What makes the project particularly interesting from a technology perspective is how Nishad built it.
Reports suggest the website was created using AI-assisted “vibe coding” — a growing approach where developers use AI tools to generate and refine software through natural-language instructions rather than writing every line of code manually.
For a BTech student, the project is an interesting example of how AI is lowering the barrier to building functional civic-tech platforms.
The technology itself is relatively straightforward. The bigger innovation lies in what happens when those individual submissions are collected, organised and made searchable.
Built with AI-assisted coding, Bribes.fyi shows how technology can help turn everyday citizen experiences into searchable civic data. Photograph: (AI generated)
Instead of a bribe remaining a private exchange between two people, Bribes.fyi attempts to turn it into a data point — one that someone else can search before visiting an office, compare with reports from other cities, or use to understand whether similar experiences are being reported elsewhere.
The platform is still evolving, and questions around verifying user submissions remain. But its premise is simple: if technology can make it easier for citizens to document what usually goes undocumented, even everyday experiences can become part of a much bigger picture.
