Top 50 Synthetic Data Platform

DataCebo provides an AI-powered synthetic data platform that enables enterprises to generate and manage synthetic datasets for machine learning applications, significantly reducing reliance on real data. By utilizing advanced generative models developed from MIT research, the platform allows teams to perform 90% of their data work without compromising privacy or data integrity.

Gretel is a multimodal synthetic data platform that utilizes generative AI and privacy-enhancing technologies to create artificial datasets that mirror the statistical properties of real data. This enables developers to train and validate AI models while maintaining data privacy and accelerating access to high-quality data.

Parallel Domain provides a synthetic data platform that generates high-fidelity camera, LiDAR, and radar data for training and testing AI perception systems. This technology enables developers to simulate diverse scenarios in procedurally generated environments, reducing the risks and costs associated with real-world data collection.

Provides a platform that generates high-quality synthetic data and automates data profiling, enabling organizations to improve data quality, protect sensitive information, and accelerate AI model development. By replacing or augmenting real datasets with statistically accurate synthetic alternatives, it reduces time-to-market by up to 50% and enhances model performance by up to 20%.

AuraML offers a synthetic data platform that utilizes Generative AI to create pre-labeled images with pixel-perfect annotations, enabling computer vision teams to generate customized datasets efficiently. This solution addresses the challenges of manual data collection and labeling, significantly reducing costs and time while enhancing dataset quality and model accuracy.

Betterdata provides a data platform that generates programmable synthetic data to replace sensitive production data, ensuring compliance with data protection laws. This approach enables faster access to realistic data for product development and testing while mitigating privacy risks associated with sharing actual data.

Develops a platform that combines AI agents with consumer hardware to generate high-quality, low-cost synthetic data at scale. This addresses the challenge of data scarcity and quality in training AI/ML models, enabling faster and more efficient model development.

Synthesis AI offers a synthetic data generation platform specifically designed for computer vision applications, enabling the creation of privacy-compliant and unbiased datasets. This technology addresses the need for high-quality training data in areas such as biometric identification, autonomous vehicle behavior simulation, and augmented reality, facilitating faster model development and deployment.

Rockfish Data offers a generative data platform that creates privacy-preserving synthetic data tailored for diverse enterprise datasets, enhancing data usability while maintaining security. This solution addresses data sparsity and sharing restrictions, enabling organizations to operationalize outcome-centric analytics effectively.

The startup offers a data-sharing platform that utilizes data mining, artificial intelligence, and deep learning to create synthetic data that preserves the statistical properties of original datasets. This enables private and public companies to share data and machine learning models securely, facilitating software testing and analysis without compromising data integrity.

MOSTLY AI provides a platform that generates fully anonymous synthetic data using optimized Generative AI models, enabling organizations to share sensitive data while ensuring compliance with privacy regulations like GDPR and CCPA. This technology addresses the challenge of restricted data access for AI/ML development and analytics, allowing teams to derive insights and improve model performance without compromising data privacy.

Hazy provides synthetic data solutions that generate realistic datasets while preserving the statistical properties of the original data, enabling organizations to utilize their data without compromising privacy. This technology addresses the challenge of inaccessible enterprise data due to privacy regulations, allowing businesses to enhance decision-making, accelerate AI development, and drive innovation.

SKY ENGINE AI provides a Synthetic Data Cloud that generates multimodal synthetic data for training deep learning models in computer vision, significantly reducing the need for real-world image acquisition. This technology enhances model accuracy by up to 4150% and accelerates AI development cycles by up to 3340 times, addressing the challenges of data scarcity and high costs in various industries such as automotive, healthcare, and robotics.

BlueGen.ai develops AI-driven synthetic data that mimics real data while ensuring privacy through differential privacy techniques. This technology enables organizations to generate high-quality, privacy-compliant datasets for machine learning, software testing, and data sharing, significantly reducing the need for real data and minimizing privacy risks.

Rendered.ai provides a platform for generating physics-based synthetic datasets tailored for computer vision applications, enabling the creation of accurately labeled data for rare events and edge cases that are difficult to capture with real sensors. This technology addresses the challenges of data scarcity and labeling accuracy, facilitating the development and training of AI and machine learning models across various industries.

Provides an AI-powered data privacy platform that tokenizes sensitive information using synthetic data, enabling organizations to protect, govern, and comply with regulations like GDPR, CCPA, and HIPAA. The zero-trust architecture ensures that only the organization controls its data, reducing breach risks and lowering total cost of ownership while supporting over 50,000 data types and 100+ sources.

Synthesized provides a unified platform for automated test data provisioning, utilizing generative AI to create, mask, and subset production-like data tailored for development and testing teams. This approach minimizes compliance risks and accelerates development cycles by ensuring teams have access to relevant, high-fidelity data without the need for full virtualized copies.

Cognata offers a digital twin-based simulation platform that generates synthetic data and augments real-world datasets to enhance the training, testing, and validation of autonomous vehicles and advanced driver-assistance systems (ADAS). This technology addresses the challenge of insufficient and diverse data for effective development and certification of automated driving systems, accelerating their time to market.

MetAI generates high-fidelity digital twins and synthetic data to accelerate AI development and validation for industrial applications. Their platform leverages NVIDIA Omniverse and proprietary generative models to rapidly create SimReady environments, enabling faster AI training and simulation.

Tonic provides secure data transformation solutions that generate realistic synthetic data for software development and AI model training, ensuring compliance and protecting sensitive information. By automating the de-identification and synthesis of both structured and unstructured data, Tonic enables faster testing and development cycles while maintaining data integrity.

The startup provides privacy-guaranteed synthetic data generated through advanced algorithms for training AI models. This approach mitigates the risks associated with using real data, ensuring compliance with data protection regulations while enhancing model accuracy and performance.

Provides tools for creating high-quality, synthetic datasets and fine-tuning small specialized models using generative AI. This addresses the challenge of limited access to tailored, multimodal datasets necessary for training and evaluating advanced machine learning models, improving their accuracy and reliability.

Aetion Generate utilizes generative AI to produce privacy-preserving synthetic datasets from real-world and clinical data, enabling organizations to share and analyze sensitive information without compromising privacy. This technology addresses the challenge of accessing and utilizing valuable data while mitigating re-identification risks and enhancing dataset completeness for improved insights.

Aetion offers a cloud‑native platform that ingests heterogeneous health data (EHR, claims, registries) and provides guardrailed ETL pipelines, validated causal‑inference analytics, and AI‑driven synthetic data generation to produce regulatory‑ready real‑world evidence. Its visual dashboards, APIs, and compliance controls enable biopharma, med‑tech firms, and regulators to generate reproducible evidence in weeks rather than months.

Invisibly develops a hybrid research platform that combines AI-driven insights with consented human data to create Synthetic Audiences for market research. This approach provides businesses with fast, statistically significant insights while ensuring alignment with real user behavior, addressing the need for reliable data in decision-making.

Lemon AI generates high-quality synthetic data to enhance the training and fine-tuning of large language models (LLMs), addressing the scarcity and quality issues of real-world datasets. By automating data curation and integrity analysis, Lemon AI enables organizations to build customized LLMs more efficiently, reducing time and costs associated with manual data preparation.

The startup develops data visualization software that utilizes synthetic data generation to replace personal data and rebalance biased datasets, enabling secure and fair analysis. This technology allows businesses to leverage AI opportunities for comprehensive data insights, enhancing operational efficiency and decision-making.

NayaOne is a fintech platform that provides a secure Digital Sandbox for enterprises to test new technologies and access synthetic data, streamlining the proof of concept process and vendor evaluations. This approach addresses lengthy procurement cycles and high experimentation costs, enabling faster time-to-market for financial solutions.

Subsalt provides a query engine that automates the anonymization of regulated enterprise data, ensuring compliance with data protection laws without lengthy legal processes. By generating high-quality synthetic data that preserves row-level granularity, Subsalt enables organizations to share sensitive information quickly and securely with internal teams and partners.

Zetamotion's Spectron platform provides AI-powered visual inspection for manufacturers, enabling rapid onboarding of new product variants with synthetic data. It delivers real-time defect detection, measurement, and classification to improve production yield and reduce quality control costs.

Fairgen is a data debiasing platform that utilizes generative AI to enhance survey sampling by creating synthetic boosters for under-sampled groups, effectively increasing data reliability. This technology enables researchers to achieve the insights of three times more real data, addressing the challenge of representation in market research.

The startup develops a human-centric AI dataset platform that retrofits existing machine learning models to provide interpretable explanations for their decisions. This technology enables businesses to identify and address bias, inaccuracy, and inefficiency through the generation of high-quality synthetic data, enhancing decision-making processes.

Synth is an open-source data generation tool that uses a declarative configuration language to create realistic, anonymized datasets for development, testing, and continuous integration. It addresses the need for privacy-compliant data by enabling users to generate consistent data models that mimic production environments without exposing sensitive information.

Provides a privacy-preserving analytics and AI platform that enables data scientists and analysts to query sensitive data without direct access, using differential privacy, synthetic data generation, and privacy-first query rewriting. This approach ensures compliance with data protection regulations, prevents breaches, and allows organizations to unlock the full value of their data while maintaining security and privacy.

Provides an AI-powered platform for generating synthetic research data and automating data analysis across qualitative and quantitative studies. By eliminating the need for traditional research samples, it reduces time and costs while delivering actionable insights through tools like Virtual Audiences, Count, Summarize, and Gen.

Dedomena provides a platform for data anonymization and synthetic data generation, ensuring compliance with data protection regulations while maintaining data utility. The technology enables businesses to create high-quality, statistically similar datasets for testing, validation, and AI model improvement, significantly reducing project timelines and costs.

The startup develops an AI-driven platform for creating three-dimensional simulations and synthetic data, utilizing extended reality to produce immersive environments. This technology reduces the time and costs associated with data labeling for training and building information modeling applications.

Syntho is an AI synthetic data platform that generates realistic synthetic data twins by mimicking statistical patterns of original datasets, enabling businesses to create secure, representative test data for non-production environments. This approach minimizes the risks associated with handling sensitive information while accelerating development cycles and ensuring compliance with data privacy regulations.

Opendatabay is a secure data marketplace that enables users to discover, access, and monetize live data assets, including synthetic and premium datasets, for AI and research projects. The platform addresses the challenge of data scarcity by providing a centralized repository for diverse data sources, facilitating easier data acquisition and collaboration among researchers, developers, and businesses.

Another Earth utilizes synthetic data generation techniques to create high-fidelity datasets for the Earth Observation industry, enabling more accurate AI model training. This approach addresses the scarcity of real-world data by providing scalable, diverse, and privacy-compliant datasets for environmental analysis and monitoring.

Nurdle generates high-performance synthetic datasets using a kernel of real conversational data, ensuring 100% privacy compliance and human-level accuracy. This approach significantly reduces the time and cost of acquiring labeled datasets, enabling faster AI model development and deployment for various applications, including intent detection and chatbot optimization.

Capoom provides a 3D synthetic data generation platform that creates customizable datasets for AI training, significantly reducing the time and costs associated with real-world data collection. The platform enables industries such as autonomous driving and urban planning to utilize realistic simulations and digital twins, enhancing model accuracy and diversity.

AI Verse provides a self-service platform that generates high-quality, fully labeled synthetic image datasets using procedural technology for training computer vision applications. This solution addresses the challenges of acquiring real-world data by enabling users to customize scene parameters and produce diverse datasets quickly and efficiently.

Fantix is an AI platform that utilizes federated learning and synthetic data generation to enhance business data science while ensuring consumer privacy and business confidentiality. Its solutions, including Yellowcake for consumer data enrichment and Fusion for audience targeting, enable businesses to gain actionable insights and improve marketing precision without compromising user data.

Datamynd offers a synthetic data generator that enables data teams to create highly accurate synthetic datasets while ensuring the protection of sensitive information. This solution addresses the challenge of data privacy by facilitating secure data sharing and analytics without compromising security.

Anyverse provides a synthetic data generation platform that creates high-quality datasets for training and validating AI perception models in automotive applications. This technology addresses the need for reliable and diverse data to enhance system performance and reduce the risks associated with real-world testing.

Scenario provides a cloud‑native platform that generates photorealistic synthetic image and video datasets with automatic pixel‑accurate annotations such as bounding boxes, segmentation masks, and depth maps. Users configure virtual scenes via a parametric editor or API, and the system applies domain randomization and style transfer to reduce the sim‑to‑real gap, delivering data that integrates directly into common machine‑learning pipelines. The service scales on demand for computer‑vision teams in autonomous driving, robotics, retail analytics, and AR/VR.

The startup provides synthetic data generation tools that enhance computer vision model training by creating diverse and realistic datasets. This approach mitigates the challenges of data scarcity and privacy concerns, enabling more accurate and robust AI applications in various industries.

Diveplane offers the Howso platform, which utilizes causal AI and synthetic data to enhance data validation and model monitoring while ensuring transparency and auditability. This approach enables organizations to maximize the utility of their data, significantly reducing time and costs associated with traditional AI workflows.

The startup develops a real-time data generation platform that utilizes generative algorithms to create custom datasets with pixel annotations and structured spatial data. This technology enables data scientists to rapidly iterate models while ensuring improved accuracy and effective bias controls in their datasets.