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After five years at Google building search and AI systems that served billions, Abhimanyu Chopra walked away to solve a problem most technologists would never think twice about: the immense friction involved in organizing and playing sports.
There is a peculiar gap in the sporting world. Through school, university, and professional academies, sports are meticulously organized for you. There are teams, schedules, fields, and coaches. Outside of those structured environments, however, much of that infrastructure vanishes. Managing a local team, joining a league, or even playing a casual game of football on a weekday evening suddenly becomes a logistical puzzle; one that limits participation globally.
Chopra noticed this pattern in every city he lived in, from Bangalore to San Jose, from the Bay Area to Delhi. The specifics changed, but the frustration was always the same: finding enough players, coordinating schedules, reserving a field, collecting payments. By his estimate, the organizational burden of a single game or team practice could easily consume two hours before anyone touched a ball.
“Playing sports should be as easy as finding a cab,” he says. “You open an app, you see a game near you, you join. That is the bar. And right now, we are nowhere close to that for most athletes.”
Chopra is the founder and CEO of Flickmatch, an AI-powered sports matchmaking platform that is attempting to fundamentally rethink how people organize, play, and analyze sports. The platform currently operates across four cities, including San Jose, Gurugram, Hyderabad, and Raipur, and has grown to over 5,500 users.
The intersection of two obsessions
Chopra’s background makes the venture less surprising than it might appear at first glance. By his own account, he has always lived at the intersection of two passions: technology and sports. He captained his university football team at NSUT in Delhi and played competitively through school. When his career took him across continents, the one constant frustration was how difficult it remained to simply get a game going or find a team.
On the technology side, his credentials run deep. Chopra spent over five years at Google, where his work touched some of the company’s most critical AI and search infrastructure. He built transliteration models for Google Search that changed 6% of search results across India, covering Hindi, Bengali, Tamil, Telugu, and five other Indian languages. He then moved to conversational AI during the early rollout of Google’s large language models, where he helped improve support agent efficiency across all Google products. Most recently, he built the price extraction pipeline for Google Shopping, a system that processes millions of merchant web pages daily to surface accurate, real-time pricing for consumers across the globe.
It was an enviable career trajectory by any measure. But the desire to build something of his own, something that solved a problem he personally experienced every week, never went away.
The problem nobody is solving well
The community and amateur sports market is what investors would call a blue ocean. Once athletes step outside of top-tier institutional infrastructure, they are left to figure things out on their own. The tools available to them are, generously speaking, primitive.
“The way most people play and organize sports today is through WhatsApp groups,” Chopra explains. “Someone has to be the host. That person coordinates timing, collects payments from everyone, reserves the field, handles cancellations. It is a part-time job that nobody signed up for. And what inevitably happens is the host burns out, the group dies, and people stop playing.”
AI as the host
Flickmatch’s central thesis is straightforward: the host, the person who does all the coordination work, should be replaced by an AI agent.
The platform uses AI-powered assistants that handle natural language interactions with players. These agents manage scheduling, payment collection, field reservations, and cancellations, automating the entire coordination workflow. According to Chopra, the AI has reduced manual operational overhead by approximately 80% compared to human-run equivalents.
The technical architecture reflects Chopra’s experience building at Google scale. The platform runs on a Java and Spring backend with GraphQL APIs, uses DynamoDB for data persistence and Firebase for real-time features, and deploys a React frontend through modern CI/CD pipelines. The AI layer integrates Google’s Gemini models for natural language game operations.
Beyond coordination: Video analytics
Solving the logistical nightmare of scheduling was only the first step. Chopra is now leveraging his AI background to bring professional-grade analytics to athletes at every level. Flickmatch recently integrated a deep learning-based video statistics generator to analyze the games being played on its platform.
By capturing match footage, Flickmatch’s computer vision models process the video to extract individual performance metrics, track movement patterns, and automatically clip highlights. This turns any standard match into a fully quantified athletic experience, elevating the standard of sports outside of the professional leagues.
Building the social graph of athletes
While logistics and analytics are the functional core of the platform, Chopra recognized that the true stickiness of sports is the community. To address this, Flickmatch is expanding beyond utility to become a dedicated social network for players.
“When athletes leave institutional structures, they don’t just lose the sports infrastructure; they lose the social circles that come with it,” Chopra notes. “We are rebuilding that community layer using technology.”
Under Chopra’s architecture, the platform allows users to build athletic profiles, track their match history, and connect with teammates. By leveraging AI to suggest connections and games based on a player’s skill level, location, and playing style, Flickmatch acts as a hyper-localized social network. It ensures that a player who moves to a new city can instantly plug directly into an active, relevant community of peers.
Rapid growth across four cities
In just a few months since launch, Flickmatch has already built a thriving community. The platform facilitates over one game every single day in Gurugram and Hyderabad, runs three to four games a week in Raipur, and hosts one to two weekly sessions in San Jose. What started with football has already expanded into tennis and pickleball.
The speed of this growth is notable. There are no boots on the ground. The AI handles coordination, the video analytics keep players engaged, the social network connects them, and the community keeps growing.
The bigger picture
“There is a wave coming,” Chopra says. “More people want to play. The demand is real. What has been missing is the infrastructure to make it frictionless. If we get that right, the market size is enormous, because the addressable population is essentially everyone who loves sports but is blocked by logistics.”
For someone who spent half a decade teaching machines to understand language and extract data at Google’s scale, the problem of getting players onto a field might seem trivial. Chopra sees it differently.
“This is what I care about. Sports and technology,” he says. “The coordination problem in sports is a real-time, multi-variable optimization challenge that just happens to look like a WhatsApp group chat.”
Whether Flickmatch becomes the dominant platform in this space remains to be seen. But the underlying bet is that AI can unlock a massive, underserved segment of the sports economy by removing organizational friction, adding professional-level data, and building a global athletic network. Early traction increasingly supports this vision. The games are happening. The players are showing up. And the host, for the first time, never gets tired.
