Deccan AI raises $25 million to build high-accuracy AI systems for enterprises and frontier labs

Mercor competitor Deccan AI raises $25M, sources experts from India | TechCrunch

As demand grows for training and refining AI models, Deccan AI -- a startup supplying post-training data and evaluation work -- has raised $25 million in its first major funding round, with much of that work carried out by an India-based workforce of experts. The all-equity Series A round was led by A91 Partners, with participation from Susquehanna International Group and Prosus Ventures. While frontier AI labs including OpenAI and Anthropic build core models in-house, much of the post-training work -- from data generation to evaluation and reinforcement learning -- is increasingly being outsourced as companies push to make systems reliable in real-world use. Deccan is emerging as one of a new set of startups serving that demand. Founded in October 2024, Deccan provides services ranging from helping models improve coding and agent capabilities to training systems to interact with external tools such as application programming interfaces (APIs), which connect AI models to software systems. The startup works with frontier labs on tasks such as generating expert feedback, running evaluations and building reinforcement learning environments, while also serving enterprises through products including its evaluation suite, Helix, and an operations automation platform. The work is also evolving as models move beyond text into so-called "world models" that better understand physical environments, including robotics and vision systems. Deccan's customers include Google DeepMind and Snowflake, according to the company. It has onboarded about 10 customers and runs a couple of dozen active projects at any given time, founder Rukesh Reddy (pictured above) said in an interview. The startup, headquartered in the San Francisco Bay Area with a large operations team in Hyderabad, employs about 125 people and relies on a network of more than 1 million contributors, including students, domain experts, and PhDs. Around 5,000 to 10,000 contributors are active in a typical month, Reddy told TechCrunch. About 10% of Deccan's contributor base has advanced degrees such as master's and PhDs, though the share is higher among active contributors depending on project requirements, Reddy said. The market for AI training services has expanded rapidly alongside the rise of large language models, with companies such as Meta-owned Scale AI and its rival Surge AI, as well as startups Turing and Mercor competing to provide data labeling, evaluation, and reinforcement learning services. "Quality remains an unsolved problem," Reddy said, adding that tolerance for errors in post-training is "close to zero" as mistakes can directly affect model performance in production. That makes post-training more complex than earlier stages, requiring highly accurate, domain-specific data that is harder to scale. The work is also highly time-sensitive, he said, with AI labs sometimes requiring large volumes of high-quality data within days, making it difficult to balance speed with accuracy. The sector has faced criticism over working conditions and pay, with large pools of gig workers often used to generate training data. Reddy said earnings on Deccan's platform range from about $10 to $700 per hour, with top contributors earning up to $7,000 a month. Even as its customers are largely U.S.-based AI labs, most of Deccan's contributors are based in India. Competitors such as Turing and Mercor also source contractors from the country, but operate across a broader set of emerging markets. Deccan chose to concentrate much of its workforce in India to better manage quality, Reddy said. "Many of our competitors go to 100-plus countries to find the experts," he said. "If you have operations in just one country, it becomes far easier to maintain quality." That approach highlights India's current position in the global AI value chain -- as a supplier of talent and training data rather than a developer of frontier models, which remain concentrated among a handful of U.S. companies and a few players in China. However, Reddy said Deccan has begun sourcing talent from a few other markets, including the U.S., for niche expertise in geospatial data and semiconductor design. Reddy said Deccan was built as a "born GenAI" company, in contrast to traditional data labeling firms that began with computer vision tasks. This means it has focused on higher-skill work from the outset. Deccan grew 10x over the past year and is now at a double-digit million-dollar revenue run rate, Reddy said, declining to share specifics. About 80% of its revenue comes from its top five customers, reflecting the concentrated nature of the frontier AI market, he added.

Deccan AI raises $25 million from A91 Partners, SIG, Prosus Ventures, eyes enterprise customers

Deccan AI has raised $25 million in a round led by growth equity firm A91 Partners, with participation from Susquehanna International Group (SIG) and Prosus Ventures, as it doubles down on building high-accuracy AI systems for enterprises and frontier model labs, the company's founder Rukesh Reddy told ET. Prosus-backed artificial intelligence startup Deccan AI has raised $25 million in a round led by growth equity firm A91 Partners, with participation from Susquehanna International Group (SIG) and Prosus Ventures, as it doubles down on building high-accuracy AI systems for enterprises and frontier model labs, the company's founder Rukesh Reddy told ET. For A91 Partners, which has backed companies such as Blue Tokai Coffee, Healthkart, Aye Finance and Giva, this is the first investment in an artificial intelligence startup. "We've reached a tipping point where the industry is moving past the chatbot phase. Getting an agent to work in a demo is one thing; getting it to handle high-stakes business logic is another," he said. Reddy added that while large language models have improved rapidly, their probabilistic nature makes them inconsistent in real-world deployments, especially in enterprise settings where the cost of error is high. The company is looking to fill this gap. Deccan started with data and model training for frontier AI labs and now has built a platform-based approach. It now combines data, reinforcement learning environments, and human expertise to improve models. This has helped the company expand into enterprise offerings where Deccan will help enterprises deploy, evaluate, and improve AI systems end-to-end rather than just supplying training data. "One is an evaluation suite that helps enterprises monitor whether AI models are performing reliably in production. The second is a broader enterprise platform focused on automating back-office and middle-office operations using AI agents, deployed within the client's infrastructure with a hybrid AI-plus-human model," he said. For enterprises, the startup has rolled out products such as 'Helix', a hybrid human-plus-automated evaluation suite, and 'EnterpriseOS', which focuses on automating back- and middle-office workflows. Reddy said the company is primarily targeting Fortune 500 firms, with global capability centres (GCCs) serving as a key entry point. Hiring for enterprise customers Deccan AI is also setting up a Bengaluru office focused on enterprise business, complementing its existing presence in San Francisco and Hyderabad. The team in Bengaluru is expected to grow from a handful of employees to 20-30 over the year. Reddy, an IIT Bombay alumnus, said so far the business was entirely focused on frontier model training six months ago. "It is now roughly 90% frontier and 10% enterprise, but the enterprise segment is expected to grow faster as AI deployment scales. This mirrors a broader industry shift from model training to inference and real-world usage." On what differentiates Deccan from AI SaaS or other workflow players, Reddy said its experience working with frontier AI labs, which gives it insight into how models behave and fail. "This allows the company to deploy AI in enterprise settings with higher accuracy, especially in complex environments like legacy systems without APIs. It also positions itself as an orchestrator-combining the best available models and tools rather than building everything from scratch." Overall Reddy said the nature of work is also shifting on the AI front. New skills such as prompting, evaluation, and system oversight are emerging. Kaushik Anand, partner at A91 Partners said Deccan is building the essential infrastructure for the next decade of software. "As the world moves from experimentation to execution, the need for Deccan's evaluation and monitoring layer becomes non-negotiable. They are making it safe for consumers and enterprises to actually trust AI."