TLDR: Indian startups are increasingly developing synthetic data platforms to address critical challenges in AI training, such as data scarcity, privacy concerns, and the need for accurate labeling. By generating artificial datasets that mimic real-world statistical patterns, companies like Indika AI, Kroop AI, and AuraML are enabling more ethical, scalable, and robust AI model development across various sectors.
The landscape of Artificial Intelligence (AI) development in India is witnessing a significant shift, with a growing number of startups focusing on the creation of synthetic data platforms. This innovative approach is designed to overcome inherent limitations associated with real-world data, including its scarcity, the complexities of privacy regulations, and the labor-intensive process of accurate data labeling. Synthetic data, which comprises artificially generated datasets engineered to replicate the statistical characteristics of actual data, is emerging as a crucial enabler for advanced AI training.
In the competitive global AI arena, synthetic data offers a multifaceted solution. It allows developers to circumvent data scarcity, significantly reduce the need for manual labeling, and safeguard privacy by avoiding the direct use of sensitive individual records. This data can be generated through various methods, including statistical resampling, rule-based generation, learned models like Generative Adversarial Networks (GANs), and sophisticated simulation pipelines. Its utility extends to filling gaps for missing or rare scenarios, providing perfectly accurate labels, and easily scaling to build comprehensive model test environments. To ensure accuracy and reliability, and to prevent bias or temporal shifts, synthetic data undergoes rigorous checks, including matching real data patterns, model testing, and maintaining audit records.
India’s regulatory environment, particularly with the Digital Personal Data Protection (DPDP) Act, 2023, permits the use of synthetic data generally, provided it cannot be traced back to real individuals. Startups in this domain are therefore emphasizing provenance documentation and anonymization to ensure compliance. For instance, a finance company can simulate credit card spending patterns of a specific cohort using synthetic datasets, thereby training models without exposing personally identifiable information of real customers, assuming the synthetic generation does not inadvertently reproduce real records. This controlled methodology supports both legal compliance and ethical AI development.
Several Indian startups are at the forefront of this innovation:
AuraML: Founded in 2022 by Ayush Sharma and Arjun Gupta, this Bengaluru-based deeptech startup specializes in synthetic dataset solutions and multimodal world models for robotics and vision AI. Their flagship platform, auraSim, is a generative simulation tool designed to bridge the “sim-to-real” gap by replicating real-world complexity for robotics training and AI model development. Its features include text-to-3D environment generation, advanced LiDAR and camera sensor noise modeling, cloud-based multi-robot testing, AI-assisted labeling, and a proprietary synthetic data rendering engine.
Indika AI: Based in Mumbai and founded by Hardik Dave and Anshul Pandey, Indika AI is a data-centric AI startup offering synthetic data generation, advanced data annotation, labeling, and AI model fine-tuning solutions. They create artificial datasets that mirror the statistical properties of real data, spanning tabular formats, unstructured text, images, and audio. Their solutions address privacy, security, compliance, and accessibility challenges in highly regulated sectors such as finance, healthcare, and legal technology.
Kroop AI: This Gandhinagar-based startup, established in 2021 by Jyoti Joshi, focuses on deepfake detection and generative AI for video content. Synthetic audio-visual data forms the core of their technology. Kroop AI leverages advanced, ethical synthetic data generation to create diverse, high-quality training datasets. These datasets power their multimodal deep learning models for detecting manipulated media across video, audio, and images, and for generating text-to-video content through digital avatars in over 25 Indian languages. The use of synthetic data significantly enhances the robustness, accuracy, and scalability of Kroop AI’s solutions, which serve sectors like BFSI (Banking, Financial Services, and Insurance), e-commerce, pharma, and cybersecurity.
Also Read:
- Generative AI Propels India’s Next Wave of Application Modernization and Digital Transformation
- ET Soonicorns Summit 2025: Charting India’s AI Trajectory from Infrastructure to Ethical Frameworks
These companies exemplify India’s growing prowess in developing sophisticated AI infrastructure, paving the way for more efficient, ethical, and scalable AI applications across industries.
