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

Deccan AI's funding signals a structural shift in how AI training infrastructure gets built and scaled. The deliberate concentration of expertise in India reflects cost arbitrage, yes, but more importantly it reveals how data quality and labeling—unglamorous but critical—are becoming differentiators in RAG and fine-tuning pipelines. For data engineering teams, this matters because our tooling choices increasingly depend on who controls the training data supply chain. If companies like Deccan AI succeed at managing quality at scale from distributed regions, we'll see pressure to build tighter integrations between our data platforms and specialized training services. This likely means more API-first architectures, better lineage tracking for training datasets, and harder questions about data provenance in our dbt models. My recommendation: audit how your organization currently handles training data ingestion. Most teams treat it as a black box import problem rather than a first-class data product. Start documenting data quality SLAs for ML inputs now, before external vendors force standardization on you.