Ensure AI performance in edge cases with privacy-safe, bias-free synthetic data
Building top-notch AI systems require millions of clean, labelled records. In their absence training pipelines are held up and scalability becomes a major issue. HitechDigital provides scalable, bias-free, and scalable datasets for computer vision, predictive analytics and deep learning workflows.
As a leading synthetic data generation service provider for machine learning, we create high-quality synthetic data with embedded annotations that preserve privacy and reduce labelling costs. Our synthetic data augmentation using structured, unstructured and edge-cases to up model generalization and training efficiency. Our synthetic data solutions ensure that you get large, balanced and high-quality datasets to overcome data scarcity, data privacy risks and pesky unbalanced datasets.
We leverage statistical modelling, simulation engines and good old generative AI to build domain-specific synthetic datasets that are as realistic. Our experts design custom data simulation services that mimic real-world conditions, all of which adds up to speeding up the training of your AI model. And every project we take on includes consultation, synthetic data design, generation, QA validation and delivery – the works. We are equipped to handle complex scenarios like synthetic data for computer vision with pixel level accuracy while being on the same page as you in terms of compliance, transparency and model goals.
80 %
Faster data availability
90 %
Reduced labeling effort
100 %
Privacy-compliant datasets
80 %
Drop in annotation costs
98 %
Improved simulation accuracy
Get smart synthetic datasets for training your AI.
Request Your Synthetic Dataset →Purpose-built synthetic data services for every AI workflow.
Boost model performance by adding simulated variations to your dataset to increase diversity, balance and learning depth at scale.
Get structured & unstructured synthetic data for your domain, simulating edge cases and hard-to-source scenarios for model training.
Get expert guidance to plan and deploy AI synthetic data solutions aligned to your goals, quality metrics, privacy, and use case specifications.
Simulate custom datasets with precision using our expert-led data simulation services for various object types, behaviors and conditions.
Get labeled synthetic data for computer vision models for detection, segmentation and image-based ML workflows.
Our QA process ensures your synthetic data for AI model training meets realism, distribution and accuracy standards before deployment.
Datasets are delivered faster with embedded labels, accelerating AI project timelines.
Automated labeling of synthetic data for machine learning eliminates manual annotation and third-party labeling.
Class imbalance is addressed through controlled simulation of rare and underrepresented data scenarios.
Augmented datasets with diverse samples improve model robustness across real-world conditions.
Synthetic dataset generation eliminates exposure to sensitive information and enables regulatory-safe development.
Data is custom-generated for specific objects, behaviors and scenes—ideal for model stress-testing.
We manage your synthetic data pipeline from design to validation.
We simulate data for specific industries, objects and learning objectives.
Every dataset is accurate, realistic and model-ready.
Our workflows are streamlined for large-volume delivery.
We support any size project with flexible engagement and scaling options.
Our synthetic data ensures zero exposure of sensitive or real data.
AI and ML companies should use synthetic data generation and augmentation services for AI model development as real data is typically scarce and super expensive. Using our synthetic data generation services will help you with fast development, precision accuracy, and rigorous compliance all at the same time.
Synthetic data for AI model training lets you fake scenarios we rarely see, really tough edge cases and balanced pics all at once – this gets you to model convergence way faster, your model generalizes better and it doesn’t get poisoned with bias.
Hybrid datasets are common but models that are driven by vision and simulation will often get way more out of totally synthetic data, especially when real data is either super rare or super sensitive.
Leading synthetic data generation companies use advanced simulation engines, domain-specific models, and GANs (Generative Adversarial Networks) to replicate realistic distributions, behaviors, and edge cases. Many also perform QA and validation against real datasets.
Synthetic data AI can be designed to include underrepresented classes or rare edge cases, helping to balance datasets and reduce bias. This leads to fairer, more accurate machine learning models across diverse user groups or scenarios.
Vision-based deep learning, predictive maintenance, robotics – and all sorts of AI models that need to be self-sufficient – tend to love this too – because those types of models really need a whole lot of diverse data.
Our quality control process covers a few angles; visual checks to make sure it looks right, statistical sanity checks to make sure it adds up and domain mapping – all so the dataset we hand you is properly aligned with what you’re trying to use it for. We do this with the help of our data simulation services, and custom modelling tailored to each domain.
