How AI Labs Get Training Data: Enterprise Data, Experts and Ops

How does private and expert-created data reach AI labs? Two Deccan AI leaders explain enterprise data, expert networks and data operations. AI labs are now buying private company data, from enterprise workflows to internal code and chat archives. What has to happen to that data before a lab can train on it? Arpit Maheshwari sources the data and the experts. Shakeel Imdad runs delivery. In this episode of Source Code, Arpit Maheshwari (Growth and Data Partnerships) and Shakeel Imdad (Delivery) talk about where AI training data comes from and what it takes to make it usable. They discuss why frontier labs are going after messy, real-world enterprise data, why mid-training has become a term to know and what happens to a company’s data before it reaches a lab, from cleaning and anonymization to compliance with rules such as GDPR and India’s DPDP Act. The conversation then turns to data operations: how delivery has changed, why ops teams now need to understand ML, LLM judges and the latest research and how quality gets built into tasks that are longer, multi-step and agentic. They also cover why subject matter experts have become the key bottleneck in the pipeline, what draws experts to AI training and how evals and benchmarks help enterprises weigh open-source models against closed ones. About Source Code Source Code is Deccan AI’s leadership on the record: conversations, opinion segments and rapid fires, straight from the people building Deccan AI. About Deccan AI Based in San Francisco, Deccan AI helps frontier AI labs and enterprises improve model performance through research-grade evals, benchmarks and novel datasets at scale. With 1.2M+ experts and platforms like STARK, Helix and EnterpriseOS, we deliver mid-training and post-training solutions across Agentic, Coding, Multimodal, Model Alignment, Robotics and more. Looking for a data and evaluation partner? Reach out at hey@deccan.ai. #AIData #EnterpriseAI #SourceCode