Indian artificial intelligence (AI) firms are increasingly becoming the first users of their own AI agents, deploying them across functions such as customer support, coding, sales and operations before offering them to enterprise clients. The approach, known as Inside Out AI, is emerging as a key strategy for companies seeking to improve reliability, identify failures and demonstrate measurable business value at a time when enterprises are demanding greater proof of AI’s effectiveness before deployment.
Unlike traditional software products that are tested and refined largely through pilot programmes and customer feedback, AI agents operate in dynamic environments where outcomes can be unpredictable. As a result, firms such as Gupshup, CoRover, Wingify and Blackstraw AI are increasingly using their own workplaces as testing grounds, allowing employees to interact with AI systems in real business scenarios before they are commercialised. The trend mirrors practices adopted by global technology firms including OpenAI, Anthropic, Microsoft, Google and Salesforce, which extensively use their own AI systems internally to understand performance, limitations and adoption challenges.
Lab-to-Market Gap
Industry executives say the shift is being driven by growing customer expectations. Enterprises investing in AI are increasingly looking for evidence of productivity gains, cost savings and operational improvements before committing to large-scale deployments. “Having an Inside Out AI strategy allows AI companies to present evidence of successful results and measurable value to customers, which ultimately builds trust. This also ensures that the security, governance and compliance standards are met,” Kanishk Agrawal, AI expert and CTO of Judge Group India, told Fe.
Gupshup is among the companies that adopted the approach while developing its SuperAgent platform. Before its broader rollout in April, the company extensively used the product internally to understand how AI agents behave under real-world conditions. “The biggest edge of Inside Out AI is that it lets you prove value in the real world before asking customers to trust it. When we use our own agents across support, engineering, coding, lead qualification and operations, we are testing against the complexity and unpredictability of an actual business,” Beerud Sheth, CEO and co-founder, Gupshup, said. CoRover’s founder and CEO Ankush Sabharwal said internal deployments help expose the gap between laboratory performance and real-world performance, where business environments are often far more dynamic than benchmark tests suggest.
Phased Evolution
For companies pursuing the strategy, internal deployment is not a one-time exercise but a phased process. According to Ankit Jain, co-founder and CPTO of Wingify, AI systems typically begin in a “shadow mode”, where they generate recommendations while humans retain full control. They then move to supervised autonomy, where agents can execute low-risk actions subject to human approval, before eventually progressing to bounded autonomy with strict guardrails and rollback mechanisms. “It can take weeks to get a capability into shadow mode and a quarter or two before it earns bounded autonomy. Some capabilities never graduate,” Jain said. The approach is gaining traction as India’s AI ecosystem expands. According to IMARC Group, the country’s AI market, valued at $1.6 billion in 2025, is projected to reach $13.2 billion by 2034. Data from Tracxn shows India has more than 500 funded AI startups that have collectively raised over $6 billion.