CodeAnt Aims to ‘Stand on the Shoulders of AI Coding Giants’
As the AI coding race heats up, so does the race of AI debugging and reviewing tools. With competitors like SonarQube and CodeRabbit in the mix, CodeAnt AI is quickly gaining pace and is poised to outperform others. Most recently, CodeAnt AI has raised $2 million in seed funding to accelerate its mission of cutting manual code review time and bugs by over 50%. The round was led by Y Combinator, Uncorrelated Ventures, and VitalStage Ventures, with participation from DeVC, Transpose Platform, Entrepreneurs First, and several prominent angel investors. This marks CodeAnt AI’s first institutional funding, valuing the startup at $20 million. The funds will be used to expand the company’s engineering and business development teams as it scales adoption of its AI code reviewer, which is already in use at more than 50 companies, including Akasa Air, Cyient, Bureau, and Kuku FM, as well as several Fortune 1000 enterprises. For security-focused organisations, CodeAnt AI offers the flexibility to operate entirely within their own infrastructure, ensuring data privacy and compliance. Founded by Amartya Jha and Chinmay Bharti in 2023, CodeAnt AI was born out of their shared frustration with inefficient code reviews. Jha, who previously scaled infrastructure at Zeta and ShareChat, witnessed critical bugs slipping through during inadequate reviews. The journey began at Entrepreneur First, where Jha met Bharti. The company quickly gained traction, securing a spot in YC by November 2023. By February 2024, they had launched their first product, attracting major clients like Tata 1mg, India’s largest online pharmacy, and Cipla, one of the country’s biggest pharmaceutical companies. While speaking with AIM, Jha said that the platform supports more than 30 programming languages, which is a moat for the company. While competition in the AI-driven code review space is growing, Jha remains confident in CodeAnt AI’s unique value proposition. Its biggest competitor is SonarQube. However, interestingly, its lead investor is also one of the investors of CodeAnt. On the other hand, CodeRabbit relies solely on AI, making it not an exact competitor. “The demand for tools like ours is only going to grow as AI-generated code becomes more prevalent,” Jha said. According to him, tools like GitHub Copilot or Cursor are far from generating accurate code anytime soon. Customers and Investors Sing Praises Giving one of the examples at that time, Jha said that Tata 1mg had written hundreds of custom policies on their platform. “Before CodeAnt AI, they would have had to build a similar platform themselves to enforce these policies,” Jha said, adding that Tata 1mg has written code in Python for the last eight years. Now, they can simply use the CodeAnt platform to ensure that their code complies with their specific guidelines, which is especially valuable for large organisations with complex codebases. With the latest funding round, Jha is even more optimistic. “Most software bugs and vulnerabilities slip through at the peer review stage, where issues could have been caught early and cheaply. CodeAnt AI is built to do both—helping companies move faster and stay competitive without compromising on security or code quality,” he said. Investors agree on CodeAnt AI’s potential. Tom Blomfield, a partner at Y Combinator, emphasised the growing importance of review processes in an AI-first coding environment. “With more and more code being generated by AI, code review has never been more important. CodeAnt fits into your CI/CD pipeline and ensures that only high-quality code makes it into production. Not AI-generated slop!” The platform integrates seamlessly with GitHub, GitLab, Bitbucket, and Azure DevOps, offering real-time feedback across more than 30 programming languages. In addition to flagging quality and security issues, CodeAnt AI suggests one-click fixes, turning lengthy reviews into quick five-minute tasks. This results in fewer deployment delays, reduced post-release bugs, and lower remediation costs—up to 10 times cheaper when issues are addressed during reviews rather than after deployment. CodeAnt AI’s core differentiator lies in its proprietary language-agnostic AST engine, which understands how different parts of a codebase interact. This allows it to identify complex issues that isolated static analysis tools might miss. The platform also incorporates threat intelligence from major security databases and enables organisations to configure rules aligned with their internal coding standards. With clients like Bureau, Kuku FM, and Akasa Air, CodeAnt is making the rounds. Bureau and Kuku FM echoed similar sentiments when they said that CodeAnt AI is highly accurate in detecting code defects and potential issues, with heavy customisation. Engineering leaders have already seen tangible benefits. Adil Khanday, software architect at Akasa Air, said, “The platform’s SCA and SAST capabilities have also enabled us to enhance our security posture, identifying vulnerabilities and weaknesses before they become major issues.” Meanwhile, Salil Deshpande, general partner at Uncorrelated Ventures, said, “I love that CodeAnt stands on the shoulders of giants such as SonarQube.” He added that it dramatically speeds up code reviews by identifying and fixing issues early during the review process, which is one of the most manual and critical parts of software development today.
CodeAnt AI has raised $2 million in seed funding to accelerate its mission of cutting manual code review time and bugs by over 50%.
CodeAnt AI has raised $2 million in seed funding to accelerate its mission of cutting manual code review time and bugs by over 50%.
["AI in Coding", "CodeAnt"]
["Go", "AI", "R", "ML", "CodeAnt", "Git", "AI in Coding", "Python", "Aim", "Rust", "GitHub", "Azure"]
["AI", "ML", "Aim", "Azure", "Python", "R", "Go", "Rust", "Git", "GitHub"]
Mohit writes about AI in simple, explainable, and often funny words. He's especially passionate about chatting with those building AI for Bharat, with the occasional detour into AGI.