Services
Creation of high-fidelity, artificially generated datasets that bypass real-world data scarcity and preserve privacy, solving the cold start problem while ensuring regulatory compliance. Sub-services include synthetic transaction data for AML training, differential privacy synthetic data generation, healthcare EHR synthetic data modeling, and multimodal synthetic data creation.
End-to-end engineering of enterprise-grade synthetic data platforms, enabling scalable, on-demand generation and management of high-fidelity datasets to solve data scarcity and accelerate AI initiatives.
Development of synthetic datasets using differential privacy and other advanced techniques to ensure regulatory compliance (e.g., GDPR, HIPAA) while preserving the statistical utility of the original sensitive data.
Creation of photorealistic synthetic image and video datasets using generative adversarial networks (GANs) and neural radiance fields (NeRFs) to train robust object detection and segmentation models without real-world data collection.
Generation of realistic, multivariate time-series data for predictive maintenance, financial forecasting, and IoT analytics, capturing complex temporal dependencies and seasonality patterns.
Creation of high-fidelity synthetic transaction and behavioral datasets to train and stress-test fraud detection AI models, simulating rare but critical attack patterns and adversarial scenarios.
Design and generation of adversarial and edge-case synthetic datasets specifically for stress-testing AI models, identifying failure modes, and improving generalization before production deployment.
Design and implementation of automated, production-ready data pipelines for continuous synthetic data generation, validation, and integration into existing ML training and testing workflows.
Rigorous testing and validation services for synthetic datasets, ensuring statistical fidelity, feature correlation integrity, and fitness-for-purpose using metrics like TSTR (Train on Synthetic, Test on Real).
Generation of multimodal synthetic environments and sensor data (LiDAR, radar, camera) for training and validating autonomous vehicles, drones, and robotics in safe, simulated conditions.
Leveraging state-of-the-art generative models (e.g., diffusion models, LLMs) to create complex, structured synthetic datasets for NLP, tabular data, and multimodal applications, solving cold-start problems.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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