Deccan AI founder Rukesh Reddy after the company raised $25 million in Series A funding to scale enterprise AI evaluation, post-training infrastructure, and production-grade AI systems.
Series A Round Led by A91 Partners Validates Post-Training Infrastructure Demand
Deccan AI, an artificial intelligence startup focused on post-training data and enterprise AI evaluation services, secured $25 million in Series A funding led by A91 Partners, with participation from Susquehanna International Group and existing investor Prosus Ventures.1 The capital enables expansion of enterprise-grade AI infrastructure as companies transition from experimental pilots toward high-stakes operational deployments.
Founded by Rukesh Reddy, Deccan AI grew 10x last year while working with top frontier AI laboratories and enterprises including a majority of the Magnificent Seven tech giants. The company operates the full post-training stack for AI model development, combining expert human trajectories, rigorous evaluation protocols, and reinforcement learning environments preparing models for mission-critical applications.2
For enterprises, Deccan AI delivers Helix, a hybrid human and automated evaluation suite monitoring AI reliability and model performance. EnterpriseOS, the scaled operations automation suite, transforms fragile AI pilots into production workflows using secure agents.3 Both products address the critical challenge of moving AI from proof-of-concept to business-critical deployment where errors carry significant cost implications.
The capital deployment will fund expansion of post-training infrastructure, research investment, enterprise-grade infrastructure development and robotics automation solutions.4 Deccan AI has established operations in San Francisco and Hyderabad, with recent Bengaluru office launch targeting India’s growing enterprise technology market.5
Founder Reddy emphasised the industry inflection: getting agents to work in demos differs fundamentally from handling high-stakes business logic requiring reliability and auditability. The funding validates market thesis that post-training data, evaluation infrastructure and operationalisation tooling represent critical software layers enabling AI adoption across enterprise environments.
