In a significant leap towards efficiency, coding automation is swiftly advancing within the startup ecosystem. Currently, many startups have reached automation levels between 15% and 50%, with aspirations to elevate this range to 40% to 85% by the end of 2025. This shift is driven by the widespread adoption of generative AI tools, reshaping how software is developed, tested, and deployed.
From e-commerce and fintech to SaaS and AI, startups are keen to streamline development workflows, enhance productivity, and enable engineering teams to focus on higher-value innovation. For example, adtech firm InMobi has automated 50% of its software coding and is aiming for 80% automation by the end of the year. Similarly, B2B e-commerce platform Udaan has automated 90% of its front-end development and 30% to 50% of its back-end systems.
K Siddhartha Reddy, the company’s senior vice president and head of engineering, mentioned that Udaan is empowering every developer with AI-driven tools to boost productivity and redirect focus towards architecture, innovation, and solving user-centric problems. Backed by WestBridge Capital, InMobi has integrated AI tools like GitHub Copilot to automate 20% to 30% of routine coding tasks, targeting 75% automation for non-differentiated code such as quality assurance and unit testing, and 50% for core production code by year’s end. Vikas Boggaram Setty, vice president of engineering at LeadSquared, noted that their strategy involves embedding generative AI across the software development lifecycle, from project planning to post-release analysis, utilizing smarter models for seamless integration into development environments.
Conversational messaging platform Gupshup has automated 35% of its coding workflows, aiming to scale to 70-75% shortly.
Coding automation boosting startup efficiency
This aligns with its focus on early-stage testing, reusable code components, and expanding AI-assisted development tools.
Conversely, conversational AI startup CoRover has achieved 40% automation in repetitive code generation, testing, and deployment, with plans to reach 65% by the year’s end through enhanced AI models. Freshworks, another notable example, has seen a 30% reduction in coding time and 61% improvement in code quality and technical debt reduction, thanks to AI integration. GenAI startup Gnani AI has automated 25% to 30% of its routine coding tasks, targeting an increase to 40% to 50% this year.
While tech-heavy sectors like SaaS and e-commerce lead this automation push, other industries are catching up. Education platform PhysicsWallah has also made strides in automation, although specific figures were not disclosed. Investment platform InvestorAi, currently at 15% automation, aims to scale to 75% by the end of 2025.
Startups emphasize that the goal of automation is not to replace human developers but to eliminate repetitive, low-value tasks, allowing engineers to focus on critical thinking, user experience, and system optimization. Recognizing AI’s limitations in nuanced understanding and contextual judgment for complex software design, companies invest in reskilling programs to transition developers to roles requiring deeper technical expertise, such as AI/ML engineering and solution architecture. They also create opportunities in emerging areas like AI ethics, model training, and collaborative workflows, aiming to integrate automation in a way that enhances, rather than displaces, human talent.
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