Business Situation
Before AIKosh existed, India’s AI data landscape was fragmented and inefficient. Datasets were scattered across government ministries, universities, hospitals, and private organizations, creating significant barriers for AI development. Data engineers wasted countless hours searching for, cleaning, and validating information before they could even begin training AI models. The situation was further complicated by expensive access to private datasets and inconsistent quality standards due to lack of validation frameworks.
The absence of a unified platform meant that accessing reliable GPUs for training was costly and limited, often requiring expensive outsourcing arrangements. This fragmented ecosystem was hampering India’s AI advancement at a national level.
To address these critical challenges, IndiaAI partnered with NeGD and Unthinkable to create a comprehensive solution. The technical requirements included:
Centralized data aggregation: Consolidate datasets, models, and resources from ministries, universities, hospitals, and private contributors into a single, searchable platform
Robust validation framework: Implement multi-level review processes to ensure only verified and trustworthy datasets reach publication
Clear licensing structure: Establish transparent usage rights and access rules for each dataset
Flexible access controls: Enable contributors to determine sharing levels - from open access to request-based or private workspace usage
Quality assurance system: Develop scoring mechanisms based on data uniqueness, completeness, and update frequency
Integrated development environment: Provide GPU-enabled sandbox for direct experimentation and model training
Scalable architecture: Design infrastructure capable of seamlessly onboarding additional organizations over time
