Services
Synthetic Data Generation and Augmentation
Synthetic Data Generation and Augmentation
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.
Synthetic Data Platform Development
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.
Privacy-Preserving Synthetic Data Engineering
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.
Synthetic Data for Computer Vision
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.
Synthetic Time-Series Data Development
Generation of realistic, multivariate time-series data for predictive maintenance, financial forecasting, and IoT analytics, capturing complex temporal dependencies and seasonality patterns.
Synthetic Data for Fraud Detection Systems
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.
Synthetic Data for Model Robustness Evaluation
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.
Synthetic Data Pipeline Architecture
Design and implementation of automated, production-ready data pipelines for continuous synthetic data generation, validation, and integration into existing ML training and testing workflows.
Synthetic Data Quality Assurance and Validation
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).
Synthetic Data for Autonomous Systems Training
Generation of multimodal synthetic environments and sensor data (LiDAR, radar, camera) for training and validating autonomous vehicles, drones, and robotics in safe, simulated conditions.
Generative AI for Data Fabrication
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.
Partnered with leading AI, data, and software stack.
How We Work
Custom AI workflows for your Business
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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Review the use case
We understand the task, the users, and where AI can actually help.
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Pick the right approach
We define what needs search, automation, or product integration.
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Build the first useful version
We implement the part that proves the value first.
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Improve from there
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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