In the competitive world of AI, synthetic data—artificially generated datasets designed to mirror real-world statistical patterns—enables developers to overcome data scarcity, reduce manual labelling, and protect privacy by avoiding direct use of real individual records.
Collected from statistical resampling and rule-based generation, learned models like GANs (Generative Adversarial Networks), and simulation pipelines, synthetic data can fill gaps in missing or rare scenarios, provide perfectly accurate labels, and easily scale to build model test environments.
With proper checks—such as matching real data patterns, testing with models, and keeping audit records—this data stays accurate, reliable, and helps avoid bias or changes over time.
In India, synthetic data is generally permissible if it cannot be linked back to real individuals. With the Digital Personal Data Protection (DPDP) Act, 2023, synthetic outputs derived from identifiable data may still be regulated.
Most startups, therefore, emphasise provenance documentation and anonymisation to ensure compliance. For instance, a finance company using a synthetic dataset to simulate credit card spending patterns of a niche cohort can train models without exposing real customers’ personally identifiable information—provided that synthetic generation doesn’t inadvertently reproduce real records. This controlled approach supports legal and ethical adoption.
Here are some startups building synthetic data platforms and solutions.
Indika AI
The Mumbai-based data-centric AI startup provides synthetic data generation, advanced data annotation, labelling, and AI model fine-tuning solutions.
Founded by Hardik Dave and Anshul Pandey, creates artificial datasets that mirror the statistical properties of real data—starting with tabular formats and expanding to unstructured text, images, and audio—addressing privacy, security, compliance, and accessibility challenges in regulated sectors like finance, healthcare, and legal tech.
Further, the synthetic data enables AI model training, testing, and validation while preserving critical insights and ensuring privacy compliance.
Indika AI is also developing programmatic labelling tools to automate annotation for real and synthetic datasets, with use cases such as generating synthetic credit card transaction data to model underrepresented user segments, or producing privacy-safe medical datasets for clinical AI development.
Onix AI
Headquartered in New York with offices in Pune, Hyderabad, Bengaluru, San Francisco, and Ottawa, the enterprise tech company specialises in cloud, data, and AI-driven business solutions. Its expertise spans AI-powered analytics, data modernisation, and agentic AI through its Wingspan platform.
Onix AI’s key offering—the Kingfisher Synthetic Data Generator—is a zero-code, AI-powered tool that analyses production data and business logic to create statistically accurate, privacy-preserving synthetic datasets, free from personal identifiers, for AI training, testing, and development.
Built for regulated sectors such as finance, healthcare, retail, and telecom, Kingfisher helps companies address privacy, scarcity, and compliance challenges while scaling data securely from kilobytes to petabytes.
Integrated with Wingspan and other Onix tools, the platform enables safe, efficient AI innovation, supported by its broader capabilities in predictive analytics, personalisation, fraud detection, and cloud optimisation.
Kroop AI
The Gandhinagar-based startup, founded in 2021 by Jyoti Joshi, specialises in deepfake detection and generative AI for video content, with synthetic audio-visual data at the core of its technology.
Using advanced, ethical synthetic data generation, Kroop AI creates diverse, high-quality training datasets that power its multimodal deep learning models for detecting manipulated media across video, audio, and images, as well as for generating text-to-video content through digital avatars in over 25 Indian languages.
Synthetic data helps enhance the robustness, accuracy, and scalability of Kroop’s AI solutions, which cater to the BFSI, ecommerce, pharma, and cybersecurity sectors.
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Boltzmann
Founded in 2019 by Kolli Sarath, the Bengaluru-based AI-driven biotechnology startup uses Gen AI, large language models, and synthetic data to accelerate drug discovery and improve clinical trial success.
Its platforms include BoltChem for designing novel drugs; ReBolt for generating synthetic synthesis pathways to optimise R&D; BoltBio (in beta) for identifying disease root causes; ClinBolt for predicting clinical trial outcomes; and BoltPro for AI-driven protein engineering.
Synthetic data is central to Boltzmann’s approach, with AI-generated molecular datasets, simulated protein structures, and synthetic pathways enabling faster, more accurate predictions in drug design, molecular property exploration, and clinical research—helping drug manufacturers improve timelines and efficiency.
AuraML
Founded in 2022 by Ayush Sharma and Arjun Gupta, the Bengaluru deeptech startup specialises in synthetic dataset solutions and multimodal world models for robotics and vision AI.
Its flagship platform, auraSim, is a generative simulation tool that bridges the “sim-to-real” gap by replicating real-world complexity for robotics training and AI model development.
Its features also include text-to-3D environment generation, advanced LiDAR and camera sensor noise modelling, cloud-based multi-robot testing, AI-assisted labelling, and a proprietary synthetic data rendering engine.
Serving industries like warehouse automation, industrial robotics, and autonomous systems, AuraML enables faster iteration, safer deployment, and scalable AI integration through realistic, tailored synthetic data for computer vision and robotics applications.
Edited by Suman Singh
