Apr 7, 2025

is advancing at an unprecedented pace across the startup ecosystem, with companies currently reporting automation levels between 15% and 50% and targeting 40% to 85% by the end of 2025. What once seemed like a distant vision has quickly become a strategic priority as startups adopt generative AI tools to reimagine the way software is written, tested, and deployed. From e-commerce and fintech to SaaS and AI, startups are racing to streamline development workflows, enhance productivity, and free up engineering teams to focus on higher-value innovation. Adtech firm InMobi is already halfway to its goal, having automated 50% of its software coding. CEO Naveen Tewari recently said in a LinkedIn post that the company is targeting 80% automation by the end of the year. B2B e-commerce platform Udaan is not far behind, with 90% of its front-end development and 30% to 50% of its back-end systems already automated. The company's senior vice president and head of engineering, K Siddhartha Reddy, told Fe that Udaan is empowering every developer with AI-driven tools to increase productivity ten-fold and redirect focus toward architecture, innovation, and user-centric problem solving. backed by WestBridge Capital, has integrated AI tools such as GitHub Copilot and Claude Code to automate 20% to 30% of routine coding tasks, such as writing boilerplate code, generating unit tests, and handling CRUD operations. The company is targeting 75% automation of undifferentiated code, including quality assurance, DevOps, and unit testing, and aims for 50% automation of core production code by the end of the year. Vikas Boggaram Setty, vice president of engineering at LeadSquared, said the company's strategy involves embedding generative AI throughout the software development life-cycle – from project planning to post-release analysis – using smarter models that can better interpret developer intent and integrate seamlessly into development environments. Conversational messaging platform Gupshup has automated 35% of its coding workflows and plans to scale that to 70-75% in the coming months. The company is focusing on early-stage testing, reusable code components, and expanding its use of AI-assisted development tools. Similarly, conversational AI startup CoRover has reached 40% automation, largely in repetitive code generation, testing, and deployment. Founder and CEO Ankush Sabharwal said CoRover's AI-powered framework, which uses natural language processing and machine learning, enables code synthesis, bug detection, and parts of the re-factoring process. The company expects to push automation to 65% by year-end by expanding model coverage to areas like AI-assisted code reviews, optimisation, and broader framework integration. Freshworks has also been reaping the benefits of AI in development. A recent company blog noted that its software engineers have seen a 30% reduction in coding time, and 61% reported improvements in code quality and a reduction in technical debt. GenAI startup Gnani AI has automated around 25% to 30% of its routine coding tasks and aims to raise that to 40% to 50% this year through a mix of internal models and external developer productivity tools. While high-growth and late-stage startups in traditionally tech-heavy sectors like SaaS and e-commerce are leading the push, other sectors are catching up. Education platform PhysicsWallah confirmed to Fe that it has made significant strides in automation, though it didn't disclose specific figures. Investment platform InvestorAi is currently at 15% automation and is running experiments with AI agents to automate large-scale updates. It aims to scale to 75% by the end of 2025. Despite the aggressive automation targets, startups emphasise that their goal is not to replace human developers. Instead, automation is being deployed to eliminate repetitive, low-value tasks so engineers can focus on critical thinking, user experience, and system optimisation. AI, they acknowledge, still lacks the nuanced understanding and contextual judgment needed for high-stakes or complex decision-making in software design. To support this shift, many companies are investing in reskilling programmes to transition developers into roles requiring deeper technical expertise, such as AI/ML engineering and solution architecture. Startups are also creating opportunities in emerging areas like AI ethics, model training, and collaborative workflows, with the broader goal of integrating automation in a way that enhances rather than displaces human talent.