Alternatives to MDClone

Windocks provides on-demand Oracle, SQL Server, as well as other databases that can be customized for Dev, Test, Reporting, ML, DevOps, and DevOps. Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management. Databases can be delivered to conventional instances, Kubernetes or Docker containers. Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments. When combined with Docker containers, enterprises often see a 5:1 reduction of lower-level database VMs.

Overcome challenges when acquiring data to train AI and machine learning systems. Rendered.ai, a PaaS, is designed for data scientists and engineers. Create synthetic datasets to train and validate ML/AI. Experiment with scene content, sensor models, and post-processing. Catalogue and characterize real and synthetic datasets. Download or move data into your own cloud repositories to be processed and trained. Synthetic data can be used to boost innovation and productivity. Create custom pipelines for modeling diverse sensors and computer-vision inputs. Python sample code is available for free and can be customized to model SAR, RGB Satellite imagery, and other sensor types. Flexible licensing allows for almost unlimited content creation. Create labeled, high-performance computing content quickly in a hosted environment. No-code configuration allows data scientists and engineers to collaborate.

Mask, generate, subset, virtualize, and automate your test data with the DATPROF Test Data Management Suite. Our solution helps managing Personally Identifiable Information and/or too large databases. Long waiting times for test data refreshes are a thing of the past.

Create high-fidelity 3D environments and diverse synthetic data quickly and easily to improve model performance. Bifrost is the fastest and easiest way to create high-quality synthetic images to improve ML performance. By avoiding time-consuming and expensive real-world data collection, you can prototype and test up 30x faster. Generating data to account rare scenarios that are underrepresented in the real data will result in more balanced datasets. Manual annotation and labeling can be resource-intensive and prone to errors. Quickly and easily generate data that are pre-labeled, pixel-perfect. Real-world data can inherit biases from the conditions under which it was collected and generate data that solves for these instances.

Tonic automatically creates mock datasets that preserve key characteristics of secure data sets so that data scientists, developers, and salespeople can work efficiently without revealing their identities. Tonic creates safe, de-identified data from your production data. Tonic models your production data from your production data to help tell a similar story in your testing environments. Safe and useful data that is scaled to match your real-world data. Safely share data across businesses, teams, and borders to create data that is identical to your production data. PII/PHI identification and obfuscation. Protect your sensitive data by proactive protection with automatic scanning, alerts and de-identification. Advanced subsetting across diverse database types. Fully automated collaboration, compliance, and data workflows.

Syntheticus®, a data exchange platform, overcomes limitations such as scarcity and bias in data, while enabling data exchange at scale. Our synthetic data platform allows you to generate high-quality, compliant data samples that are tailored to your business goals and analytics needs. Synthetic data allows you to tap into a variety of high-quality resources that aren't always available in real life. You can conduct more reliable research by using high-quality data that is consistent. This will lead to better products, business decisions, and services. You can accelerate product development cycles by having reliable, fast data at your fingertips. Synthetic data is designed as a private and secure data source by default. This protects sensitive data and maintains compliance with privacy laws.

Accelerate data processing tasks such as structuring, labeling and preprocessing. Manage all your data on a central platform that is easy to integrate. Increase data accessibility quickly with privacy-protected synthetic data and user friendly exchange platforms. The Aindo platform for synthetic data allows you to securely share data between departments, with external providers, partners and the artificial-intelligence community. Synthetic data exchange and collaboration can help you discover new synergies. Securely and openly acquire missing data. Your clients and stakeholders will appreciate the comfort and trust you provide. Aindo's synthetic data platform eliminates data inaccuracies, implicit bias and provides fair and complete insights. Enhance information to make databases resilient to special events. Balance datasets which misrepresent the true population to get a fair and accurate depiction. Fill in data gaps with precision and accuracy.

A flexible and accurate platform for the generation of synthetic data. Create the data you require for your perception system within minutes. Design scenarios with infinite variations for your use case. Create your datasets on the cloud. Anyverse is a scalable software platform that allows you to train, validate or fine-tune a perception system. It offers unparalleled computing power to generate all of the data you require in a fraction the time and cost as compared to other real-world workflows. Anyverse is a modular platform which enables efficient scene creation and dataset production. Anyverse™, Studio is a standalone application with a graphical interface that manages All Anyverse functions including scenario definition, variability setting, asset behavior, dataset settings and inspection. Data is stored on the cloud and the Anyverse cloud is responsible for scene generation, simulation and rendering.

Enterprise synthetic test data solutions. It is essential that test data accurately reflects the structure of your database or application. This means it must be easy for you to model and maintain each project. Respect the referential integrity of parent/child/sibling relations across data domains within an app database or across multiple databases used for multiple applications. Ensure consistency and integrity of synthetic attributes across applications, data sources, and targets. A customer name must match the same customer ID across multiple transactions simulated by real-time synthetic information generation. Customers need to quickly and accurately build their data model for a test project. GenRocket offers ten methods to set up your data model. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.

A self-service platform for synthetic data, with a focus on object and human data. The Datagen Platform gives you granular control over the data generation process. Analyzing your neural networks can help you understand what data is required to improve them. You can then easily generate the data that you need to train your network. Datagen is a powerful platform that can help you solve your problems. It allows you to generate high-quality, high-variety, domain-specific, simulated artificial data. Advanced capabilities include the ability to simulate dynamic people and objects within their context. Datagen gives CV teams unprecedented flexibility to control visual outcomes in a variety of 3D environments.

We can no longer rely upon real-life conversations as physical customer interactions shift to digital. Customers communicate their intentions and share their needs using data. Data is a key tool for understanding customers and testing our assumptions. Privacy regulations like GDPR and CCPA make deep understanding more difficult. This gap in customer understanding is bridged by the MOSTLY AI synthetic dataset platform. Businesses can benefit from a reliable, high-quality generator of synthetic data in many different applications. The story doesn't end there. MOSTLY AI's synthetic dataset platform is more versatile than any other synthetic data generator. MOSTLY AI's versatility makes it an indispensable tool for software development and testing. From AI training to explainability and bias mitigation, governance to realistic test data, with subsetting, referential integrity.

When capturing data in real-life situations is difficult, we create diverse, fully-labeled image datasets. Our procedural technology provides the highest-quality, unbiased, and labeled synthetic datasets to improve your computer vision model. AI Verse gives users full control over scene parameters. This allows you to fine-tune environments for unlimited image creation, giving you a competitive edge in computer vision development.

Share your production data securely with ML, data scientists, and analytics teams, while maintaining customer trust. Stop wasting time with regexes and free models. Private AI anonymizes 50+ entities PII PCI and PHI in 49 languages, with unmatched accuracy, across GDPR, CPRA and HIPAA. Synthetic data can be used to replace PII, PCI and PHI text in order to create model training data that looks exactly like production data. This will not compromise customer privacy. Remove PII in 10+ file formats such as PDFs, DOCXs, PNGs, and audios to protect customer data and comply privacy regulations. Private AI uses the most advanced transformer architectures for remarkable accuracy right out of the box. No third-party processing required. Our technology outperforms every other redaction service available on the market. Please feel free to request a copy of the evaluation toolkit for you to test with your own data.

Our AI-powered platform for data generation allows data analysts and machine-learning engineers to maximize their analytical data sets. Datomize allows users to create the exact analytical data they need by leveraging behavior extracted from existing data. With data that accurately reflects real-world scenarios and allows users to make better decisions, they can now get a more accurate picture of reality. Take advantage of your data to develop AI solutions that are state-of-the art. Datomize's AI powered, generative models create superior synthesized replicas by extracting behavior from your existing datasets. Advanced augmentation tools allow for unlimited resizing, while dynamic validation tools show the similarity of original and replicated data. Datomize's machine learning approach is data-centric and addresses the primary constraints of training high-performing ML model.

Privacy engineering tools delivered as APIs. In minutes, you can synthesize and transform data. Trust your users and the community. Gretel's APIs allow you to instantly create anonymized or synthetic data sets so that you can safely work with data while protecting your privacy. Access to data must be faster in order to keep up with the development pace. Gretel's data privacy tools bypass blockers, and allow for Machine Learning and AI applications to access data faster. Gretel Cloud runners makes it easy to scale up your workloads to the cloud or keep your data safe by running Gretel containers within your own environment. Developers will find it much easier to train and create synthetic data using our cloud GPUs. Scale workloads instantly with no infrastructure required. Invite colleagues to collaborate on cloud projects, and share data between teams.

Our AI-driven, low-code automation platform is designed for you to achieve the best quality result in the shortest time possible. Our Natural Language Processing (NLP-powered solution) allows you to generate automation scripts in plain English and allows your coders focus on innovation. Our autonomous discovery and current tracking of changes ensures that your application is high quality throughout its lifecycle. Our autonomous healing capability reduces risk in dynamic development environments and delivers flawless updates by keeping automation up-to-date. All regulatory requirements are met and security risks eliminated by using AI-generated synthetic data to automate your business processes. Multiple tests can be run simultaneously, you can determine the test frequency, keep up with browser updates, and execute across platforms and operating systems.

Synthetic data is the key to a leading-edge technology that allows for flawless retail execution. Vision technology for consumer packaged products. Choose from a catalog of over 100,000 SKUs on the Neurolabs platform, including top brands like P&G, Nestle Unilever, Coca-Cola and more. Our API will automatically stitch together multiple shelf images uploaded by your field agents from their mobile devices. SKU-level detection gives you detailed information that can be used to calculate retail execution KPIs, such as out-of shelf rate, shelf share percentage and competitor price comparison. Discover how our cutting edge image recognition technology can maximize store operations, improve customer experience, and boost profits. Implement a real deployment in less than a week. Access image recognition datasets covering over 100,000 SKUs.

With automated data quality profiling, and synthetic data generation, adopting data-centric AI is easier than ever. We help data scientists unlock the full potential of data. YData Fabric enables users to easily manage and understand data assets, synthetic data, for fast data access and pipelines, for iterative, scalable and iterative flows. Better data and more reliable models delivered on a large scale. Automated data profiling to simplify and speed up exploratory data analysis. Upload and connect your datasets using an easy-to-configure interface. Synthetic data can be generated that mimics real data's statistical properties and behavior. By replacing real data with synthetic data, you can enhance your datasets and improve your models' efficiency. Pipelines can be used to refine and improve processes, consume data, clean it up, transform your data and improve its quality.

The Synthetic Data vault (SDV) was designed as a Python library that allows you to create tabular synthetic data. The SDV uses machine learning algorithms to emulate patterns in synthetic data. The SDV offers a variety of models, from classical statistical methods to deep learning methods. Create data for single tables or multiple connected tables. Compare the synthetic data with the real data using a variety measures. Diagnose problems and create a quality report for more insights. Control data processing to enhance the quality of synthetic information, choose different types of anonymization and define business rules as logical constraints. Use synthetic data to replace real data or as an enhancement. The SDV is a comprehensive ecosystem of synthetic data models, metrics, and benchmarks.

Statice is a data anonymization tool that draws on the most recent data privacy research. It processes sensitive data to create anonymous synthetic datasets that retain all the statistical properties of the original data. Statice's solution was designed for enterprise environments that are flexible and secure. It incorporates features that guarantee privacy and utility of data while maintaining usability.

Advanced technology and services that safely transform and enhance sensitive information into actionable insights can help drive innovation and open up new revenue streams. Mimic synthetic engine allows companies to safely synthesize data assets while protecting consumer privacy and maintaining statistical relevance. The synthetic data can be used to support internal initiatives such as analytics, machine-learning and AI, marketing, segmentation, and revenue generation through external data monetization. Mimic enables you to safely move statistically-relevant synthetic data to the cloud ecosystem of your choice to get the most out of your data. With the enhanced synthetic data in the cloud, you can do analytics, insights, product testing, third-party data sharing, and more. The data has been certified as being compliant with privacy and regulatory laws.

SKY ENGINE AI is a simulation and deep learning platform that generates fully annotated, synthetic data and trains AI computer vision algorithms at scale. The platform is architected to procedurally generate highly balanced imagery data of photorealistic environments and objects and provides advanced domain adaptation algorithms. SKY ENGINE AI platform is a tool for developers: Data Scientists, ML/Software Engineers creating computer vision projects in any industry. SKY ENGINE AI is a Deep Learning environment for AI training in Virtual Reality with Sensors Physics Simulation & Fusion for any Computer Vision applications.

OneView creates next-generation virtual synthetic datasets for ML algorithm training. We provide ready-to-use datasets for all objects in any environment, with a focus on satellite and aerial imagery. VSD is computer-generated, real-like imagery. It can be used to replace real imagery. It is a cost-effective, scalable, and highly accurate training material for ML algorithm. OneView bridges the gap between increasing availability of earth observation data, and the limited ability to use it for intelligence gathering. We provide the tools to improve data analysis and enable geospatial imagery to yield new and valuable insights. It doesn't matter if you have long-tail or rare objects, or if you don't have enough data. We can generate a dataset for every object. We can generate any environment. High-detailed, customized environment for model training.

Set your enterprise data free. Hazy can re-engineer enterprise data to make it easier, safer, and faster to use. Every enterprise can actually use its data. Data has never been more valuable. However, with increasing privacy requirements and tightening regulations, the majority of the world's data remains locked away and inaccessible. Hazy has developed a new approach that allows data to be used. So you can make better decisions and develop new technologies, and provide more value to your customers. Realistic test data can be created and deployed to quickly validate new technologies and systems. This will help accelerate your digital transformation. You must generate enough high-quality, safe data to train, improve, and build your AI applications. To improve decision-making, empower teams to share and generate accurate intelligence and analytics on products, customers, operations, and other factors.

A platform for ML engineers that provides synthetic data to facilitate the development of better AI models. Simple APIs allow for the instant generation of photorealistic, well-labeled images. Cloud-based generation platform that is highly scalable delivers millions of perfectly labeled photos. On-demand data allows for new data-centric approaches to create more efficient models. A broader range of pixel-perfect labels, including segmentation maps and dense 2D/3D landmarks as well as depth maps, surface norms, depth maps, and more, is now available. You can quickly design, test, refine, and revise your products before you build hardware. To optimize your system, prototype different imaging modalities, camera placements, lens types, and other lens types. Your models will be less biased if there are misbalanced data sets. Privacy will still be protected. Equal representation across identities, facial attributes and poses, as well as lighting and camera settings. We have worked with top-notch customers in many different use cases.

Sixpack is an automated data management platform that streamlines synthetic data for testing. Sixpack, unlike traditional test data generation methods, provides an unlimited supply of synthetic data to help testers and automated testing avoid conflicts and resource bottlenecks. It focuses on flexibility, enabling data allocation, pooling and instant generation, while maintaining data quality and privacy. The key features are the ease of setup, seamless integration with APIs, and the support for complex test environments. Sixpack integrates directly into QA processes so that teams can save time managing data dependencies and minimize data overlap. Its dashboard provides a clear view into active data sets. Testers can also allocate or pool data based on project needs.

RNDGen Random Data Generator, a user-friendly tool to generate test data, is free. The data creator customizes an existing data model to create a mock table structure that meets your needs. Random Data Generator is also known as dummy data, csv, sql, or mock data. Data Generator by RNDGen lets you create dummy data that is representative of real-world scenarios. You can choose from a variety of fake data fields, including name, email address, zip code, location and more. You can customize generated dummy information to meet your needs. With just a few mouse clicks, you can generate thousands of fake rows of data in different formats including CSV SQL, JSON XML Excel.

MakerSuite simplifies this process. MakerSuite allows you to easily tune custom models, iterate on prompts and augment your data with synthetic data. MakerSuite allows you to export your prompts as code in your favorite languages, such as Python and Node.js, when you are ready to move on to code.

CloudTDMS, your one stop for Test Data Management. Discover & Profile your Data, Define & Generate Test Data for all your team members : Architects, Developers, Testers, DevOPs, BAs, Data engineers, and more ... Benefit from CloudTDMS No-Code platform to define your data models and generate your synthetic data quickly in order to get faster return on your “Test Data Management” investments. CloudTDMS automates the process of creating test data for non-production purposes such as development, testing, training, upgrading or profiling. While at the same time ensuring compliance to regulatory and organisational policies & standards. CloudTDMS involves manufacturing and provisioning data for multiple testing environments by Synthetic Test Data Generation as well as Data Discovery & Profiling. CloudTDMS is a No-code platform for your Test Data Management, it provides you everything you need to make your data development & testing go super fast! Especially, CloudTDMS solves the following challenges : -Regulatory Compliance -Test Data Readiness -Data profiling -Automation

Create, transform, or analyze any text data within your spreadsheet. Automatically normalize addresses, separate column, extract entities, etc. Rewrite SEO content or blog posts. Generate product description variations. Create synthetic data such as first/last names and phone numbers. Create bullet-point summaries and rewrite content in fewer words. Sort product feedback into categories, prioritize sales leads, find new trends, etc. Charm provides several templates to help people complete common tasks faster. Use the Summarize With Bullet Points Template to create short bullet-point summaries for long content. Use the Translate Language Template to translate existing content in another language.

Synthesized can help you unlock the full potential of your data projects and AI. We automate all stages of data preparation and provisioning with cutting-edge AI. The platform synthesizes data without exposing personal information or causing compliance issues. Software to prepare and provide accurate synthetic data for building better models at scale. Synthesized solves the problem of data sharing for businesses. 40% of companies that invest in AI can't report business benefits. Our easy-to-use platform allows data scientists, product, and marketing teams to focus on revealing critical insight. Without representative data, testing data-driven applications can be difficult. This can lead to problems when services go live.

Amazon SageMaker lets you identify raw data, such as images, text files and videos. You can also add descriptive labels to generate synthetic data and create high-quality training data sets to support your machine learning (ML). SageMaker has two options: Amazon SageMaker Ground Truth Plus or Amazon SageMaker Ground Truth. These options allow you to either use an expert workforce or create and manage your data labeling workflows. data labeling. SageMaker GroundTruth allows you to manage and create your data labeling workflows. SageMaker Ground Truth, a data labeling tool, makes data labeling simple. It also allows you to use human annotators via Amazon Mechanical Turk or third-party providers.

Data is a valuable business asset. With the right AI model it is possible to use data in order to build and understand customers profiles, identify trends, and identify potential new business opportunities. It takes a lot of data to build accurate and robust AIs, which is a challenge from both a quality and quantity perspective. Stringent regulations, including GDPR, also restrict the use and sharing of sensitive data like customer data. It's time to change the way we do things. It's time for a new approach, especially in a software test environment where it is difficult to access good quality testing data. We often see actual customer data used, which can lead to GDPR non-compliance with heavy financial penalties. Artificial Intelligence is expected to increase productivity in businesses by at least 40%, but businesses struggle to deploy AI solutions or fully unlock them due to data-related issues. ADA generates artificial data using advanced deep-learning.

As enterprise data explodes and is scattered across multiple systems, the oversight of privacy, data security and governance has become a very difficult task. Businesses are exposed to significant risks, including data breaches, privacy suits, and penalties. It takes months to find data privacy risks within an organization. A team of data engineers is involved in the effort. Data breaches and privacy legislation are forcing companies to better understand who has access to data and how it is used. Enterprise data is complex. Even if a team works for months to isolate data privacy risks, they may not be able to quickly find ways to reduce them.

Our proprietary labeling platform allows us to label your data with higher quality standards in order to meet the requirements of complex AI projects. Now you can create the products that your customers love.

dbForge Data generator for MySQL is an advanced GUI tool that allows you to create large volumes of realistic test data. The tool contains a large number of predefined data generation tools with customizable configuration options. These allow you to populate MySQL databases with meaningful data.

K2View believes that every enterprise should be able to leverage its data to become as disruptive and agile as possible. We enable this through our Data Product Platform, which creates and manages a trusted dataset for every business entity – on demand, in real time. The dataset is always in sync with its sources, adapts to changes on the fly, and is instantly accessible to any authorized data consumer. We fuel operational use cases, including customer 360, data masking, test data management, data migration, and legacy application modernization – to deliver business outcomes at half the time and cost of other alternatives.

Datanamic Data Generator allows developers to quickly populate databases with thousands upon rows of meaningful, syntactically correct data for database testing purposes. A blank database is useless for testing your application. Test data is essential. It is difficult to create your own test data generators and scripts. Datanamic Data Generator can help. This tool is available for developers, DBAs, and testers who require sample data to test a database-driven app. Datanamic Data Generator makes it easy to generate database test data. It will read your database and display tables and columns according to their data generation settings. To generate complete (realistic) test data, only a few entries are required. This tool can be used to create test data from scratch, or from existing data.

dbForge Data Generator is a powerful GUI tool that populates Oracle schemas with realistic test data. The tool has an extensive collection 200+ predefined and customizeable data generators for different data types. It delivers flawless and fast data generation, including random number generation, in an easy-to-use interface. The latest version of Devart's product is always available on their official website.

Synth is a data-as code tool that offers a simple CLI workflow to generate consistent data in an scalable manner. Synth can be used to generate data that is correct and anonymized, but still looks and sounds like production. Create test data fixtures to support your continuous integration, testing and development. Create data that tells you the story you wish to tell. Specify constraints and relations. Seed development, environments and CI. Anonymize sensitive production data. Create realistic data according to your specifications. Synth's declarative configuration language allows you to specify the entire data model in code. Synth can import existing data and create accurate data models. Synth is database-agnostic and supports semistructured data. It works well with SQL and NoSQL. Synth can generate thousands of semantic types, such as email addresses, credit card numbers and more.

Describe your data model at an abstract level using XML. It is not necessary to have developer skills. Use a variety of function libraries to create realistic data. Write your own extensions using Javascript or Java. Integrate your data processing into Gitlab CI, Jenkins or Jenkins. Benerator's model driven data toolkit allows you to generate, anonymize and migrate data. Define processes for anonymizing or pseudonymizing data in plain XML without needing developer skills. Protect your customers' privacy and stay GDPR compliant. Mask and obfuscate data sensitive for BI, testing, development or training. Subsetting allows you to combine data from different sources while maintaining data integrity. Transform and migrate your data in multisystem landscapes. Reuse your test data models for migration to production environments. In a microsystems architecture, keep your data consistent and reliable.

ArborMetrix is dedicated to advancing healthcare through data science. We are passionate about delivering high-impact, intuitive technology and analysis. Our healthcare analytics solutions have been proven to deliver clinically-relevant insights that improve clinical outcomes, optimize financial results, increase stakeholder value, measure the real-world effectiveness and efficiency of medical device technologies and pharmaceuticals. Post-market surveillance is one of the most important purposes of patient registries. These are the steps we take in order to help you establish a trusted data resource. It is essential to have a secure and efficient way to collect large amounts real-world data and prepare it in order for analysis. You can use the right technology to collect data from medical records, regulatory sources and patient-reported outcomes for post-market surveillance activities.

KopiKat, a revolutionary tool for data augmentation, improves the accuracy and efficiency of AI models by modifying the network architecture. KopiKat goes beyond the standard methods of data enhancement by creating a photorealistic copy while preserving all data annotations. You can change the original image's environment, such as the weather, seasons, lighting, etc. The result is an extremely rich model, whose quality and variety are superior to those created using traditional data augmentation methods.

PatientIQ was founded in Chicago, Illinois. It serves providers, payers, and medical device & health sciences companies. The largest platform for healthcare professionals to connect and improve patient outcomes. Provide healthcare professionals with the industry's best technology to practice data-driven medicine. All stakeholders in the U.S. healthcare system are under increasing pressure. Objective measures of patient outcomes are crucial to assessing "value". Although measuring outcomes can be complex, time-consuming, expensive, and complicated due to technological barriers, they are the most valuable currency for value-based healthcare in the future. The next big market opportunity for digital health is a transparent way to measure, analyze, compare, and compare outcomes across stakeholders.

Healthcare organizations (including hospitals, clinics, and health plans) can now connect with app developers and data partners to create new FHIR-based digital services. Ensure safer and more efficient transitions across the continuum between in-patient and outpatient care. At-risk patients can be provided with targeted prevention and wellness guidance. To effectively manage chronic conditions, encourage collaboration between healthcare providers, patients, and physicians. Improve management of chronic conditions. Provide patient-centric digital services. Reduce risks during care transitions. Secure, manage, and scale APIs using an enterprise-grade platform, which is FHIR-server neutral. Access healthcare data from FHIR-ready partners, whether they are internal, external, open-source, or both. You can quickly launch digital services such as new mobile apps to fulfill the promise of patient-centric care and data interoperability.

Atropos Health offers real-time evidence based on real-world data, to help close gaps in evidence in healthcare and life science. Proven to improve clinical outcomes, accelerate the research process, and increase operational efficiency. Atropos Health transforms medical data quickly into real-world proof, closing gaps in evidence in medicine and accelerating research from months to mere minutes. Our core solution is an operating system for accelerating generative evidence. We can install medical data securely and structure it to be used downstream. Our powerful analytics tools are available to generate evidence when needed. It can be used to generate evidence on demand with our powerful analytics tools. Join Atropos Health in partnering with top health systems and companies to accelerate research, optimize trials, inform clinical decision, address care variations, and more.

Genoox is a growing and diverse community that gathers the most relevant data and delivers real-world genomic insights. These insights can be used to improve patient outcomes and shape the business of healthcare. Our solutions are changing genomics. Genoox leverages the power of its community to combine public data with community data to speed up the path from DNA sample through to clinical report. This improves patient care by making genomic information accessible and actionable at point of care. Genoox is a platform that enhances research and life science companies. It uses real-world data and evidence to power robust genomic analytics. This enables researchers to simplify complex genomic data and make important discoveries using the most advanced genetic tools. Genoox allows biosystems companies, such as DNA sequencing companies, to bundle their state-of-the art genomic engine with dedicated assays.

We help hospitals, payers, and life science companies to make the leap to value-based healthcare by comparing treatments, drug prices, processes, and costs. Our CRCA™, P&T platform empowers P&T boards to achieve data-driven improvements in quality metrics such as 30-day readmissions and average length of stay (ALOS). Our unique POPBUILDER Rx™ solution allows for comparative analyses between selected cohorts as well as CRCA's real world data index of more than 140MM longitudinal patient records. Our drug remittance dashboard analyses and reports drug remittance information across payer types and for targeted groups of high-cost drugs, down to the NDC. Our Agilum Healthcare Intelligence team and insightful analytics help hospital finance departments monitor and improve their service line profitability, profitability, productivity, revenue cycle performance, and costing.

Connected technologies, unified data and the health ecosystem enable individuals to be empowered and accelerate innovation. This will influence health outcomes. Oracle Health is creating an open platform for healthcare that includes intelligent tools to create data-driven and human-centric experiences. This platform will connect consumers, healthcare providers and payers as well as public health and life science organizations. We have the largest global EHR share and are able bring data together for clinicians, researchers, and patients to take meaningful actions, advance health and work to improve results worldwide. IDC MarketScape rated us as the largest revenue-cycle management (RCM), and we provide timely, actionable, and predictive health insights that automate processes, optimize resource allocation, and drive efficiency. Our open, extensible ecosystem allows you to accelerate innovation, take advantage of flexible infrastructure and platform resources and drive clinical intelligence.

Combining the multiomic data and medical history of an individual to deliver more personalized healthcare. Use purpose-built databases to support large-scale analyses and collaborative research across populations. Accelerate your research with scalable workflows, integrated computation tools and integrated computing. Protect patient privacy by ensuring HIPAA compliance and using built-in data access, logging and logging. AWS HealthOmics enables healthcare and life sciences organizations and their software partner to store, query and analyze genomic, transcriptionomic, or other omics data, and then generate insights using that data. Store and analyze omics for hundreds of thousands patients to understand the relationship between omics variation and phenotypes in a population. Create reproducible and traceable workflows for clinical multiomics to reduce turnaround time and increase productivity. Integrate multiomic analyses into clinical trials to test out new drug candidates.