# How AI is Changing the Way FanCode Builds, Tests and Ships

DevFeed: [How AI is Changing the Way FanCode Builds, Tests and Ships](<https://devfeed.tech/articles/how-ai-is-changing-the-way-fancode-builds-tests-and-ships-22621.md>)

Original publisher: [Read original article](<https://medium.com/dreamlockerroom/how-ai-is-changing-the-way-fancode-builds-tests-and-ships-b89c8d62c55f?source=rss----5c7a7f580b01---4>)

Author: Dream Blog

Published: 2026-01-21T12:17:03Z

Content type: article

Language: en

Sources: [Dream11 Engineering](<https://devfeed.tech/sources/dream11-engineering.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Development](<https://devfeed.tech/topics/development.md>), [pull-requests](<https://devfeed.tech/topics/pull-requests.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Latency](<https://devfeed.tech/topics/latency.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [development](<https://devfeed.tech/tags/development.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [latency](<https://devfeed.tech/tags/latency.md>), [pull-requests](<https://devfeed.tech/tags/pull-requests.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [sports](<https://devfeed.tech/tags/sports.md>), [tech](<https://devfeed.tech/tags/tech.md>), [testing](<https://devfeed.tech/tags/testing.md>), [velocity](<https://devfeed.tech/tags/velocity.md>)

## AI overview

FanCode describes how it has integrated AI into engineers' daily development workflows, including building, reviewing, testing, and shipping software for live sports. The article links this work to faster delivery and more reliable systems under load, while describing review delays and other growing pains in a lean engineering team.

## Source excerpt

By Guruwinder Rishi and Pramod Kumar At FanCode, engineers work on problems that show up live -- in front of millions of fans -- and shape how sports are experienced in real time. If building, reviewing, testing, and shipping at scale excites you, we'd love to talk. Check out open roles at FanCode. At FanCode, engineering efficiency isn't an internal KPI. It's the difference between a fan watching a last-over finish without interruption and a stream dropping at the worst possible moment. When you're building for live sports, the margin for error is near zero. We stream 10,000+ live games every year, across sports, formats, and devices -- often with millions of fans watching at the same time. At that scale, every commit, every review delay, and every flaky test eventually shows up somewhere: in latency, stability, or how confident we feel pushing a change while a match is live. Behind the scale we operate is a lean 50 member engineering team across backend, frontend, and testing -- and that's by design. The problem we've been solving for years isn't scale through headcount. It's scale through clarity, velocity, and trust in our systems, while still giving engineers the space to learn, iterate, and do some fantastic work. We've been using AI across FanCode for a while now. But what's changed over the last year is how deeply we've started integrating it into the everyday lives of engineers. This blog outlines the changes we've made to our development workflows, how we used AI, and the results we saw in terms of faster delivery and more reliable systems under load. With Scale, the Cracks That Started to Show As FanCode scaled, we began facing the classic growing pains of a fast-moving, high-output tech team. Inefficiencies that were manageable earlier started compounding quickly, and a few patterns became impossible to ignore. I. The Review Bottleneck: Pull requests routinely took 2-3 days to move from "raised" to "merged". On their own, such delays didn't seem alarming. Bu