Best Synthetic Data Generation Tools

We offer data anonymization software that generates entirely anonymous synthetic datasets for our customers. The synthetic data generated by Statice contains statistical properties similar to real data but irreversibly breaks any relationships with actual individuals, making it a valuable and safe to use asset. It can be used for behavior, predictive, or transactional analysis, allowing companies to leverage data safely while complying with data regulations. Statice’s solution is built for enterprise environments with flexibility and security in mind. It integrates features to guarantee the utility and privacy of the data while maintaining usability and scalability. It supports common data types: Generate synthetic data from structured data such as transactions, customer data, churn data, digital user data, geodata, market data, etc We help your technical and compliance teams validate the robustness of our anonymization method and the privacy of your synthetic data

While enterprise data is exploding and scattered across various systems, oversight of driving privacy, data security, and governance has become very challenging. As a result, businesses hold significant risks in the form of data breaches, privacy lawsuits, and penalties. Finding data privacy risks in an enterprise is a complex, and time-consuming effort that takes months involving a team of data engineers. Data breaches and privacy laws are requiring companies to have a better grip on which users have access to the data, and how the data is used. But enterprise data is complex, so even if a team of engineers works for months, they will have a tough time isolating data privacy risks or quickly finding ways to reduce them.

Synth is an open-source data-as-code tool that provides a simple CLI workflow for generating consistent data in a scalable way. Use Synth to generate correct, anonymized data that looks and quacks like production. Generate test data fixtures for your development, testing, and continuous integration. Generate data that tells the story you want to tell. Specify constraints, relations, and all your semantics. Seed development and environments and CI. Anonymize sensitive production data. Create realistic data to your specifications. Synth uses a declarative configuration language that allows you to specify your entire data model as code. Synth can import data straight from existing sources and automatically create accurate and versatile data models. Synth supports semi-structured data and is database agnostic, playing nicely with SQL and NoSQL databases. Synth supports generation for thousands of semantic types such as credit card numbers, email addresses, and more.

KopiKat is a revolutionary data augmentation tool that improves the accuracy of AI models without changing the network architecture. KopiKat extends standard methods of data augmentation by creating a new photorealistic copy of the original image while preserving all essential data annotations. You can change the environment of the original images, such as weather, seasons, lighting conditions, etc. The result is a rich model whose quality and diversity are superior to those produced using traditional data augmentation techniques.

Test Data Management solutions like data masking, synthetic data generation, data subsetting, data discovery, database virtualization, data automation are our core business. We see and understand the struggles of software development teams with test data. Personally Identifiable Information? Too large environments? Long waiting times for a test data refresh? We envision to solve these issues: - Obfuscating, generating or masking databases and flat files; - Extracting or filtering specific data content with data subsetting; - Discovering, profiling and analysing solutions for understanding your test data, - Automating, integrating and orchestrating test data provisioning into your CI/CD pipelines and - Cloning, snapshotting and timetraveling throug your test data with database virtualization. We improve and innovate our test data software with the latest technologies every single day to support medium to large size organizations in their Test Data Management.

Safely share your production data with ML, data science, and analytics teams while safeguarding customer trust. Stop fiddling with regexes and open-source models. Private AI efficiently anonymizes 50+ entities of PII, PCI, and PHI across GDPR, CPRA, and HIPAA in 49 languages with unrivaled accuracy. Replace PII, PCI, and PHI in text with synthetic data to create model training datasets that look exactly like your production data without compromising customer privacy. Remove PII from 10+ file formats, such as PDF, DOCX, PNG, and audio to protect your customer data and comply with privacy regulations. Private AI uses the latest in transformer architectures to achieve remarkable accuracy out of the box, no third-party processing is required. Our technology has outperformed every other redaction service on the market. Feel free to ask us for a copy of our evaluation toolkit to test on your own data.

Tonic automatically creates mock data that preserves key characteristics of secure datasets so that developers, data scientists, and salespeople can work conveniently without breaching privacy. Tonic mimics your production data to create de-identified, realistic, and safe data for your test environments. With Tonic, your data is modeled from your production data to help you tell an identical story in your testing environments. Safe, useful data created to mimic your real-world data, at scale. Generate data that looks, acts, and feels just like your production data and safely share it across teams, businesses, and international borders. PII/PHI identification, obfuscation, and transformation. Proactively protect your sensitive data with automatic scanning, alerts, de-identification, and mathematical guarantees of data privacy. Advanced sub setting across diverse database types. Collaboration, compliance, and data workflows — perfectly automated.

Privacy engineering tools delivered to you as APIs. Synthesize and transform data in minutes. Build trust with your users and community. Gretel’s APIs grant immediate access to creating anonymized or synthetic datasets so you can work safely with data while preserving privacy. Keeping the pace with development velocity requires faster access to data. Gretel is accelerating access to data with data privacy tools that bypass blockers and fuel Machine Learning and AI applications. Keep your data contained by running Gretel containers in your own environment or scale out workloads to the cloud in seconds with Gretel Cloud runners. Using our cloud GPUs makes it radically more effortless for developers to train and generate synthetic data. Scale workloads automatically with no infrastructure to set up and manage. Invite team members to collaborate on cloud projects and share data across teams.

As physical customer interactions shift into digital, we can no longer rely on real-life conversations. Customers express their intents, share their needs through data. Understanding customers and testing our assumptions about them also happens through data. And privacy regulations such as GDPR and CCPA make a deep understanding even harder. The MOSTLY AI synthetic data platform bridges this ever-growing gap in customer understanding. A reliable, high-quality synthetic data generator can serve businesses in various use cases. Providing privacy-safe data alternatives is just the beginning of the story. In terms of versatility, MOSTLY AI's synthetic data platform goes further than any other synthetic data generator. MOSTLY AI's versatility and use case flexibility make it a must-have AI tool and a game-changing solution for software development and testing. From AI training to explainability, bias mitigation and governance to realistic test data with subsetting, referential integrity.

A synthetic data platform for ML engineers to enable the development of more capable AI models. Simple APIs provide on-demand generation of perfectly-labeled, diverse, and photoreal images. Highly-scalable cloud-based generation platform delivers millions of perfectly labeled images. On-demand data enables new data-centric approaches to develop more performant models. An expanded set of pixel-perfect labels including segmentation maps, dense 2D/3D landmarks, depth maps, surface normals, and much more. Rapidly design, test, and refine your products before building hardware. Prototype different imaging modalities, camera placements, and lens types to optimize your system. Reduce bias in your models associated with misbalanced data sets while preserving privacy. Ensure equal representation across identities, facial attributes, pose, camera, lighting, and much more. We have worked with world-class customers across many use cases.

A flexible and accurate synthetic data generation platform. Craft the data you need for your perception system in minutes. Design scenarios for your use case with endless variations. Generate your datasets in the cloud. Anyverse offers a scalable synthetic data software platform to design, train, validate, or fine-tune your perception system. It provides unparalleled computing power in the cloud to generate all the data you need in a fraction of the time and cost compared with other real-world data workflows. Anyverse provides a modular platform that enables efficient scene definition and dataset production. Anyverse™ Studio is a standalone graphical interface application that manages all Anyverse functions, including scenario definition, variability settings, asset behaviors, dataset settings, and inspection. Data is stored in the cloud, and the Anyverse cloud engine is responsible for final scene generation, simulation, and rendering.

Industry-leading technology powered by synthetic data for flawless retail execution. The new wave of vision technology for consumer packaged goods. Select from an extensive catalog of over 100,000 SKUs in the Neurolabs platform including top brands such as P&G, Nestlé, Unilever, Coca-Cola, and much more. Your field agents can upload multiple shelf images from mobile devices to our API which will automatically stitch the images together to generate the scene. SKU-level detection provides you with detailed information to compute retail execution KPIs such as out-of-shelf rate, shelf share percentage, competitor price comparison, and so much more! Discover how our cutting-edge image recognition technology can help you maximize store operations, enhance customer experience, and boost profitability. Implement a real-world deployment in less than 1 week. Access image recognition datasets for over 100,000 SKUs.

Describe your data model on an abstract level in XML. Involve your business people as no developer skills are necessary. Use a wide range of function libraries to fake realistic data. Write your own extensions in Javascript or Java. Integrate your data processes into Gitlab CI or Jenkins. Generate, anonymize, and migrate with Benerator’s model-driven data toolkit. Define processes to anonymize or pseudonymize data in plain XML on an abstract level without the need for developer skills. Stay GDPR compliant with your data and protect the privacy of your customers. Mask and obfuscate sensitive data for BI, test, development, or training purposes. Combine data from various sources (subsetting) and keep the data integrity. Migrate and transform your data in multisystem landscapes. Reuse your testing data models to migrate production environments. Keep your data consistent and reliable in a microsystem architecture.

Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.