Alternatives to Modern DataOS

DataHub is a versatile open-source metadata platform crafted to enhance data discovery, observability, and governance within various data environments. It empowers organizations to easily find reliable data, providing customized experiences for users while avoiding disruptions through precise lineage tracking at both the cross-platform and column levels. By offering a holistic view of business, operational, and technical contexts, DataHub instills trust in your data repository. The platform features automated data quality assessments along with AI-driven anomaly detection, alerting teams to emerging issues and consolidating incident management. With comprehensive lineage information, documentation, and ownership details, DataHub streamlines the resolution of problems. Furthermore, it automates governance processes by classifying evolving assets, significantly reducing manual effort with GenAI documentation, AI-based classification, and intelligent propagation mechanisms. Additionally, DataHub's flexible architecture accommodates more than 70 native integrations, making it a robust choice for organizations seeking to optimize their data ecosystems. This makes it an invaluable tool for any organization looking to enhance their data management capabilities.

Cube serves as a comprehensive platform that establishes a universal semantic layer aimed at streamlining and consolidating the management and analysis of enterprise data. By revolutionizing data management practices, Cube removes the challenges associated with inconsistent models and metrics, thereby providing users with reliable data that is also primed for AI applications. This innovative platform is instrumental for organizations seeking to expand their data infrastructure by harmonizing various data sources and generating uniform metrics applicable across different teams. Tailored for enterprises, Cube enhances analytical capabilities, ensures data accessibility, and facilitates effortless access to AI-driven insights. With Cube, companies can navigate the complexities of data management more effectively than ever before.

SAP HANA Cloud serves as a fully managed in-memory database as a service (DBaaS) that operates in the cloud. Functioning as the essential data backbone for the SAP Business Technology Platform, it assimilates information from various sectors of the organization, allowing for quicker decision-making based on real-time data. This platform empowers users to construct data solutions using contemporary architectures, yielding actionable insights instantly. As the cloud variant of SAP HANA, it provides the same robust capabilities while being scalable to fit specific requirements, allowing for the processing of diverse business data and facilitating advanced analytics on live transactions with minimal need for optimization. Users can effortlessly connect to distributed data through native integrations, develop applications and tools both in the cloud and on-premises, and manage transient data efficiently. By establishing a singular source of truth, enterprises can access reliable information while ensuring security, privacy, and data anonymization, all upheld by a foundation of enterprise-grade reliability. Furthermore, SAP HANA Cloud supports the evolving needs of businesses as they adapt to dynamic market conditions.

QuickLaunch Analytics serves as an enterprise data analytics solution that empowers organizations to consolidate disparate data from various sources, such as ERP, CRM, financial, HR, and operational systems, into a cohesive, governed analytics environment, delivering quicker, actionable insights. Instead of constructing an analytics infrastructure from the ground up, it offers a Foundation Pack featuring automated data pipelines, a cloud-native data lakehouse, and Power BI semantic models, enabling seamless integration, cleansing, and governance of raw enterprise data for analytical purposes. Additionally, the platform includes Application Packs that provide pre-built, application-specific intelligence and ready-to-use semantic models customized for systems like JD Edwards, Viewpoint Vista, NetSuite, and Salesforce, effectively translating intricate data structures into easily understandable business metrics and dashboards. As a result, QuickLaunch Analytics significantly reduces the time required to gain insights from several months or years down to just weeks, all while promoting standardized metrics and reports, facilitating cross-application analysis, and enhancing self-service BI capabilities via the use of cutting-edge technologies. This approach not only streamlines data processing but also enables organizations to make data-driven decisions with greater agility and confidence.

Kater is designed specifically for both data experts and those curious about data. It ensures that all structured data products are readily accessible to anyone with a query, even if they have no experience with SQL. Kater's mission is to unify data ownership across various departments within your organization. Meanwhile, Butler securely interfaces with your data warehouse's metadata and elements, facilitating coding, data exploration, and much more. Enhance your data for artificial intelligence through features like automatic intelligent labeling, categorization, and data curation. Our services assist you in establishing your semantic layer, metric layer, and comprehensive documentation. Additionally, validated responses are compiled in the query bank to deliver smarter and more precise answers, enhancing the overall data experience. This holistic approach empowers users to leverage data more effectively across all business functions.

Anzo is an innovative platform for data discovery and integration that empowers users to locate, connect, and blend various enterprise data into datasets that are ready for analysis. With its distinctive application of semantics and graph data models, Anzo enables individuals across the organization—from expert data scientists to inexperienced business users—to actively participate in the data discovery and integration journey, crafting their own analytics-ready datasets in the process. The graph data models offered by Anzo create a visual representation of enterprise data, simplifying the navigation and understanding of complex and siloed information. By incorporating semantics, Anzo enriches the data with business context, allowing users to unify data according to shared definitions and create blended datasets that are tailored for immediate business needs. This democratization of data access not only fosters collaboration but also accelerates decision-making across various levels of the organization.

Codd AI addresses a major challenge in the analytics landscape: transforming data into a format that is genuinely suitable for business purposes. Rather than having teams dedicate weeks to the tedious tasks of manually mapping schemas, constructing models, and establishing metrics, Codd leverages generative AI to automatically generate a context-aware semantic layer that connects technical data with the language of the business. As a result, business users can pose inquiries in straightforward English and receive precise, governed responses instantly—whether through BI tools, conversational AI, or various other platforms. Additionally, with built-in governance and auditability, Codd not only accelerates the analytics process but also enhances clarity and reliability. Ultimately, this innovative approach empowers organizations to make more informed decisions based on trustworthy data insights.

The intelligent semantic layer merges data with its business context and interconnections, consolidates metrics, and speeds up the production of data products by allowing for SQL queries that are 90% shorter. Users can easily model the data using familiar business terminology, creating a shared understanding and aligning the metrics with business objectives. By defining semantic relationships that replace traditional JOIN operations, queries become significantly more straightforward. Hierarchies and classifications are utilized to enhance data comprehension. The system automatically aligns data with the semantic model, enabling the integration of various data sources through a robust distributed SQL engine that supports large-scale querying. Data can be accessed as an interconnected semantic graph, improving performance while reducing computing expenses through an advanced caching engine and materialized views. Users gain from sophisticated query optimization techniques. Additionally, Timbr allows connectivity to a wide range of cloud services, data lakes, data warehouses, databases, and diverse file formats, ensuring a seamless experience with your data sources. When executing a query, Timbr not only optimizes it but also efficiently delegates the task to the backend for improved processing. This comprehensive approach ensures that users can work with their data more effectively and with greater agility.

Reqode is a structured context layer for AI-assisted software engineering. It connects requirements, architecture, and source code into a unified, machine-readable product model that AI systems can reliably use. By preventing context drift between specs and implementation, Reqode enables consistent AI code generation, safer refactoring, and scalable development across large codebases. It provides the foundation needed to make LLM-based engineering workflows predictable and governed.

Data engineers and scientists can be 95% better at their jobs with ready access to the most flexible semantic layer, explainable AI and reusable data modelling. They can create and expand semantic models, understand data interrelationships, and run federated query to speed up time to insight. Stardog's graph data virtualization and high performance graph database are the best available -- at a price that is up to 57x less than competitors -- to connect any data source, warehouse, or enterprise data lakehouse without copying or moving data. Scale users and use cases at a lower infrastructure cost. Stardog's intelligent inference engine applies expert knowledge dynamically at query times to uncover hidden patterns and unexpected insights in relationships that lead to better data-informed business decisions and outcomes.

Alkemi's premier offering, DataLab, serves as a secure AI-driven workspace that facilitates direct connections to your organization's regulated data from platforms such as Snowflake, BigQuery, Databricks, or even straightforward CSV uploads, allowing users to pose questions in everyday language and receive immediate, clear answers, visualizations, and suggestions without needing SQL expertise or analyst assistance. Within a private and secure environment, DataLab systematically indexes and examines your data, ensuring that every insight is both traceable and verifiable, maintaining the integrity of your data and protecting intellectual property and governance. By merging the complexities of data storage with accessible decision-making, it enhances the clarity of business intelligence through conversational AI, effectively minimizing BI backlogs and accelerating decision-making across various sectors, including marketing, finance, product, sales, and operations. Furthermore, DataLab empowers data providers to transform their datasets into engaging, AI-ready experiences that can be securely navigated by buyers, facilitating faster data discovery while safeguarding the integrity of the underlying raw data. This innovative approach not only streamlines workflows but also fosters a culture of data-driven decision-making within organizations.

Enable data for AI and analytics in a business-friendly manner through smart cataloging, supported by proactive metadata and policy governance. The IBM Watson® Knowledge Catalog serves as a powerful tool for discovering data, models, and more, enhancing the self-service exploration experience. Acting as a cloud-based repository for enterprise metadata, it facilitates the activation of information for AI, machine learning (ML), and deep learning applications. Users can access, curate, categorize, and share data and knowledge assets along with their interconnections, regardless of their location. By organizing, defining, and managing enterprise data effectively, organizations can ensure they have the appropriate context to generate value for various needs, including regulatory compliance and data monetization efforts. Furthermore, it safeguards data integrity, oversees compliance and audit readiness, and fosters client trust through active policy management and the dynamic masking of sensitive information. With user-friendly dashboards and workflows that can be easily shared with colleagues or integrated with analytical tools, businesses can consume and transform data efficiently to keep pace with their operational demands. By leveraging these capabilities, organizations can enhance their decision-making processes and drive innovation across their operations.

Strategy Mosaic serves as an AI-driven universal semantic data layer and analytics framework that integrates seamlessly with an organization's current data ecosystems, enabling the unification, regulation, and expedited access to business data for analytics, AI, and reporting without the need for expensive restructuring. This platform establishes a single source of truth, ensuring consistent business definitions, metrics, and security policies are maintained across various tools and data sources, thereby aligning data from numerous systems to provide reliable and comparable insights universally. With its AI-assisted data modeling feature, known as Mosaic Studio, the platform automates essential processes such as data preparation, cleansing, enrichment, and modeling, thereby significantly reducing the time and effort required to create robust data products and semantic models. Users benefit from universal connectors that facilitate access to governed data through SQL, REST, Python, or popular business intelligence and productivity applications like Power BI, Tableau, Excel, and Google Sheets. Additionally, an in-memory acceleration engine ensures rapid query performance across a wide array of data sources, enhancing the overall efficiency of data retrieval and analysis processes. This comprehensive approach empowers organizations to make data-driven decisions with confidence and speed.

1Platform serves as a comprehensive solution in data engineering, analytics, and AI/ML, enabling organizations to consolidate, prepare, and scrutinize data to create actionable insights and business intelligence; it integrates sophisticated data engineering with generative AI, autonomous agents, and business intelligence features, empowering companies to transform unrefined data into quantifiable results and informed strategies. The platform facilitates complete data workflows, encompassing data orchestration, analytical strategies, AI-driven assistants (both agentic and generative), and pre-built machine learning models that enhance insight generation across various domains such as sales, supply chain, finance, and operations, all while simplifying data governance, integration, and readiness processes. Furthermore, Polestar’s ecosystem is enriched with tools designed for AI-enhanced decision support and analytics dashboards, prioritizing a scalable, cloud-capable infrastructure that seamlessly integrates with leading hyperscalers and partners, including Databricks, to establish a robust AI-ready data foundation that can adapt to evolving business needs. This holistic approach ensures that organizations can leverage their data more effectively, driving innovation and competitive advantage in a rapidly changing market landscape.

Solid is a data intelligence platform enhanced by AI, aimed at ensuring enterprise data is both dependable and accessible for applications in AI, analytics, and interactive data queries. It automatically identifies, documents, and constructs business-relevant semantic models utilizing a company's existing data, queries, and tools, thereby establishing a reliable foundation that AI systems can depend upon. By assessing the actual usage of data within the organization, it produces validated tables, metrics, relationships, and SQL logic that correspond with genuine business definitions. With offerings like Solid Build and Solid Analyze, teams can streamline semantic modeling, convert natural language inquiries into production-ready SQL, and ensure models are continually refreshed in response to data changes. The platform prioritizes transparency and human involvement, permitting data teams to review, adjust, and confirm AI-generated models instead of merely depending on unclear automation. This approach fosters a collaborative environment where data professionals can enhance the quality and accuracy of the models they work with.

EVIDENS is an advanced AI platform that facilitates the discovery of digital cohorts, enhances research capabilities, promotes collaboration, and enables the sharing of insights across extensive hospital data sets. With the ability to swiftly annotate and convert your data into AI-compatible formats, users can efficiently filter populations by various clinical outcomes or demographic factors. The platform allows for limitless keyword filtering by training AI models to perform targeted searches. Furthermore, users can explore and establish criteria for cohort identification with the support of intelligent AI tools. Training AI models to automate the analysis process and categorize data into distinct groups can be done quickly and efficiently. The platform outputs AI-ready cohorts to facilitate the testing of clinical hypotheses in research studies. Users can effortlessly monitor the status and tasks related to all their projects and collaborators, while also being able to observe and engage with activities aligned with their research interests. Additionally, collaborative tools are provided to enable simultaneous efforts in creating, training, and validating AI models, fostering a productive research environment. Ultimately, EVIDENS streamlines the entire research process, making it more efficient and effective for all involved.

Launch your concept in just days instead of weeks with Riff, the pioneering AI app developer. Simply express your requirements through natural language, images, or diagrams, and watch as AI constructs React UIs for you. Enhance your product by integrating it with any service, connecting to various APIs or models to unlock its complete capabilities. With Riff, you can instruct it to create Python APIs that scrape websites, gather data from different systems, and much more. We ensure that you continuously deliver value to your clientele while managing the security and infrastructure on your behalf. Whether you are an independent developer creating a micro SaaS or an established business providing online services, we offer a tailored plan that meets your specific needs. We consistently assess the most effective models available for the intelligent framework you interact with in Riff. For those looking to develop an app that leverages a particular model or service, it's essential to either provide your API key/secrets for your application or obtain them from the users of your application as needed. This way, you can maximize the app's functionality while ensuring a seamless experience for your users.

Jade Global’s DataFirst AI platform redefines how organizations approach artificial intelligence by starting where success truly begins—data. Unlike conventional approaches that rush into AI adoption, DataFirst evaluates each data domain against seven critical dimensions to provide a readiness score and highlight areas for improvement. With built-in tools for data enrichment and cleansing, companies can systematically raise their data quality from an average of 2.5 to 4.0+ readiness, ensuring reliable outcomes. The platform equips enterprises with governance frameworks, role-based accountability, and roadmaps that span from strategy development to ongoing optimization. ROI simulation and scenario modeling enable leaders to predict business impact before making significant AI investments. Designed on the basis of 500+ enterprise implementations, DataFirst AI ensures immediate value with transparent maturity assessments and improvement plans. By addressing the root causes of AI failure—poor data quality and governance—it delivers measurable benefits such as 70% faster time-to-value and 3x higher project success rates. Organizations adopting this approach can build scalable AI strategies that deliver lasting ROI.

Match Data Pro is a sophisticated tool for managing data quality that aims to integrate, cleanse, analyze, match, eliminate duplicates, and consolidate records from various files, databases, and systems with remarkable efficiency and accuracy. It features cutting-edge AI-enabled fuzzy matching and adjustable rule-based logic to identify duplicates and inconsistencies within extensive datasets, assisting users in correcting errors, standardizing formats, and generating trustworthy golden records without the need for coding expertise. The tool also offers extensive data profiling with essential metrics to identify quality concerns prior to processing, robust data cleansing functionalities for normalizing and standardizing information, along with address verification features that enhance accuracy. Furthermore, Match Data Pro is equipped with Senzing AI entity resolution and customizable matching algorithms to accommodate minor data variations, ensuring high-performance processing capable of scaling up to millions of records. Additionally, it facilitates project job automation through scheduling, reusable rules, and seamless API integrations, making it a comprehensive solution for effective data management.

Venture Capital and Private Equity investors can harness the power of alternative data to source, track, and do due diligence. Alternative data can provide rich signals about the universe of private businesses, solving the problem with limited financial or traditional data. To unlock investment insights, it is best to create a holistic view by consolidating different datasets to analyze companies' performance. Credit card data, bank transaction data, and retention cohorts. User reviews, Net Promoter Scores, product ratings. Mobile and web activity across devices and domains. With detailed query-based searches, you can find companies that are relevant to your needs. Multi-metric analysis provides performance indicators on companies. Use our AI-suggested peer comparators, or create your own peer groups for the companies that you are interested in tracking.

Extracting business information from various sources such as printed or handwritten documents, forms, checks, invoices, emails, and more is a crucial task. This process can automatically convert unstructured customer data into a structured and digital format that is ready for business use. Once processed, the valuable data can be exported seamlessly into enterprise systems, databases, lines of business, or integrated into existing workflows. Despite the ongoing digitization and automation trends, paper remains a prevalent component in business operations worldwide. Many large corporations and organizations continue to face challenges with disorganized physical and digital documents that hinder their workflow efficiency. Significant time and resources are often dedicated to implementing automated solutions that still necessitate human intervention for data processing, which can ultimately diminish productivity and inflate costs. Consequently, businesses frequently find themselves in a position where they must sacrifice either cost-effectiveness, speed, accuracy, or the confidentiality of their data. The need for an effective solution that addresses these issues is more pressing than ever.

Inflectiv is a robust data platform that transforms raw files into organized datasets tailored for AI agents and automation tasks. Users are able to upload various file types, including PDFs, documents, spreadsheets, JSON files, and even website content. Inflectiv seamlessly structures this data, enabling it to be queried through APIs, SDKs, or integrated chat agents. Rather than dealing with unstructured documents, AI agents can interact directly with well-organized datasets that facilitate filtering, querying, and delivering consistent responses. This platform enables the development of Q&A chatbots, bots for Discord and Telegram, internal knowledge assistants, and applications powered by datasets. Users have the flexibility to keep their datasets private, share them with team members, or publish them on the marketplace for broader access. Importantly, creators maintain complete ownership of their data, along with the ability to manage access, permissions, and monetization options. Inflectiv is designed to cater to both technical experts and non-technical individuals who wish to convert their existing knowledge into reusable AI-ready intelligence without the need for custom ingestion processes, fostering innovation and collaboration across various fields.

Coginiti is the AI-enabled enterprise Data Workspace that empowers everyone to get fast, consistent answers to any business questions. Coginiti helps you find and search for metrics that are approved for your use case, accelerating the lifecycle of analytic development from development to certification. Coginiti integrates the functionality needed to build, approve and curate analytics for reuse across all business domains, while adhering your data governance policies and standards. Coginiti’s collaborative data workspace is trusted by teams in the insurance, healthcare, financial services and retail/consumer packaged goods industries to deliver value to customers.

OneLayer Bridge serves as a comprehensive Zero-Trust security and asset-management solution specifically designed for enterprise-grade private 5G and LTE networks, ensuring extensive visibility, segmentation, and control over connected devices across IoT, operational technology (OT), and cellular sectors. This platform excels in real-time discovery and the identification of every device within private cellular environments, going beyond conventional IT tools to encompass routers, hotspots, SIM-enabled assets, and devices situated behind cellular NAT. Additionally, OneLayer Bridge facilitates context-aware segmentation and policy enforcement to prevent lateral movements, while providing in-depth analytics related to connectivity, quality of service, and performance indicators, all alongside robust lifecycle asset management features that include seamless onboarding, geofencing capabilities, and continuous tracking. Furthermore, the platform seamlessly integrates with existing security infrastructures and cellular core systems, effectively bridging the divide between mobile packet cores and enterprise IT/OT environments, thereby enhancing overall operational efficiency and security. Its innovative approach ensures that businesses are well-equipped to manage their connected assets securely and effectively.

Mindfuel enables business and data teams to identify and prioritize the most significant use cases by assessing them in relation to company objectives and essential metrics. Facilitate a direct link between data and analytics products and business opportunities to improve decision-making processes and optimize resource distribution, guaranteeing that every product, from its initial idea to its implementation, showcases quantifiable value and influence on the business. Foster an environment of openness and strategic insight. Compile all available resources to enhance the discovery process and encourage the reuse of data products, ensuring consistent value creation and visibility throughout the entire journey from initial conception to final adoption, while also promoting collaboration among teams to drive innovation and efficiency.

Oxen.ai is a collaborative platform designed to assist teams in managing, versioning, and operationalizing machine learning datasets from the initial curation stage to model deployment. The platform features a powerful data version control system tailored for handling large and intricate datasets, facilitating efficient versioning, branching, and sharing of datasets, model weights, and experiments. This tool empowers various stakeholders, including machine learning engineers, data scientists, product managers, and legal teams, to collaboratively review, edit, and engage with data within a streamlined workflow. Users have the option to query, alter, and oversee datasets via an intuitive web interface, command line tools, or a Python library, offering adaptability for various technical processes. By supporting the entire AI lifecycle, Oxen.ai enables teams to curate datasets, refine models, and deploy them effectively while ensuring complete ownership and traceability throughout the process. Moreover, the platform's collaborative features foster an environment where cross-functional teams can innovate and enhance their machine learning initiatives.

Accuris stands out as a premier provider of digital engineering solutions powered by artificial intelligence, delivering a comprehensive array of tools aimed at optimizing engineering workflows, improving compliance, and speeding up the pace of innovation. Their leading product, Engineering Workbench Professional, consolidates more than 2.8 million industry standards, codes, and specifications into a unified platform, allowing engineers to efficiently access and utilize essential information. Notable functionalities include an AI-enhanced semantic search, a Smart Compare feature for analyzing document versions, and micro-alerts that offer real-time notifications regarding changes in standards. Additionally, Accuris Thread supports this ecosystem by streamlining the extraction and management of requirements throughout the engineering lifecycle, which boosts traceability and fosters collaboration among teams. Another significant product, Goldfire, leverages AI technology to deliver accurate responses to technical inquiries by comprehensively understanding and analyzing extensive engineering datasets, thus empowering engineers with the knowledge they need to drive projects forward. Overall, Accuris is committed to transforming the engineering landscape through innovative solutions that enhance productivity and foster continuous improvement.

Documentation.AI is a cutting-edge platform that streamlines the process of generating, publishing, and maintaining various forms of technical documentation, including API and software guides. By utilizing advanced authoring tools, semantic organization, and automation, it ensures that documentation remains consistent, precise, and easily accessible to users. The platform empowers teams to produce well-structured content by incorporating reusable components, adaptable layouts, and version control that seamlessly integrates with Git. This dynamic approach guarantees that documentation remains current with code and product changes, significantly minimizing the chances of presenting outdated or contradictory information. Additionally, the inclusion of interactive API documentation and testing environments allows developers to effectively explore and experiment with endpoints, thereby enhancing service onboarding and user adoption. Furthermore, Documentation.AI boasts a powerful search feature that provides cited responses, turning the documentation into a dynamic knowledge repository for its users. It also supports AI-friendly standards such as llms.txt and Model Context Protocol, ensuring that the documentation is accessible and functional for both human users and artificial intelligence systems. As a result, it not only serves as a comprehensive guide for developers but also facilitates a more efficient interaction between technology and users.

As numerous universities transition to fully remote learning, many students are contemplating the idea of taking a gap year. This shift means fewer chances to forge new friendships, embark on fresh adventures, and enjoy the traditional dormitory experience. Additionally, the fact that many institutions are maintaining their tuition and fee structures, despite the reduced value of in-person experiences, makes the concept of a gap year even more appealing for those seeking cost-effective avenues for personal and academic growth. OpenSesame offers a range of online courses designed to cultivate effective and highly regarded workforces, while also collaborating with businesses eager to find talented, internship-ready candidates. Today's learners are increasingly motivated to enhance their skill sets and fulfill their ambitions. Our meticulously crafted courses are designed to support your career advancement, providing you with specialized knowledge from industry leaders to help you stay competitive. Moreover, you can unlock access to paid internship opportunities through OpenSesame’s extensive network of partners, making your gap year not just a break, but a strategic step towards a successful future. By investing your time wisely, you can emerge not only with new skills but also with invaluable professional connections.

Muspell HDP, developed by 314e Corporation, is a cutting-edge, cloud-native data warehouse tailored for healthcare operators and built on the FHIR standard to seamlessly integrate various data types like patient records, claims, and lab results into a cohesive and standardized format, which simplifies processes and reduces the costs typically linked to custom data models. Its design allows for scalability, efficiently managing data for a vast number of patients, ranging from tens of thousands to millions, while ensuring optimal performance. Additionally, it comes equipped with ready-to-use analytics dashboards and templates for key healthcare metrics, such as HEDIS, eCQM, and QIP, along with curated reference datasets like Hospital Price Transparency and NPPES, and offers managed ETL services to maintain data freshness in near-real-time. The user-friendly interface empowers non-technical users to create charts, dashboards, and queries effortlessly. Moreover, it features an infrastructure that is ready for AI applications, supporting natural language queries and predictive analytics, all while adhering to stringent compliance and security measures, including HIPAA and HITRUST standards. This comprehensive solution not only enhances healthcare data management but also fosters a more efficient approach to analytics and decision-making within the industry.

Discover valuable insights within your data by utilizing the Cortellis™ suite of life science intelligence tools, enabling you to make more informed decisions throughout the entire R&D process. We have alleviated the burden of gathering, integrating, and analyzing data, allowing you to concentrate on the essential choices necessary for expediting your products' market entry. With a unique combination of extensive, high-quality data, fortified by profound domain knowledge, industry insight, and therapeutic expertise, Cortellis reveals crucial insights that facilitate data-driven decisions, ultimately speeding up innovation. Access tailored, actionable responses to your specific inquiries throughout the R&D lifecycle, drawing from the most comprehensive and in-depth intelligence sources available. By incorporating Cortellis into your daily routine, you can significantly enhance the pace of innovation and streamline your workflow. This makes Cortellis not just a tool, but a vital partner in your path to success.

StoredIQ offers a comprehensive suite for information governance, catering to legal needs, policy management, and features like StoredIQ InstaScan. The platform effectively tackles data management issues related to compliance, e-discovery, records management, storage optimization, and data migration. It streamlines the eDiscovery process, managing everything from hold notifications to the identification, preservation, and collection of relevant data. Additionally, it includes a sophisticated file analysis tool that utilizes automation and statistical sampling to pinpoint risk areas within unstructured cloud data. By providing centralized retention controls, it assists in mitigating information management risks while aligning with business and legal obligations. Furthermore, through a thorough evaluation of unstructured data across the organization, Breakwater empowers businesses to adopt data that is ready for operational use, thereby facilitating a seamless digital transformation journey for the entire enterprise. This integrated approach not only enhances compliance but also fosters a more efficient data-driven environment.

Kyvos is a semantic data lakehouse designed to speed up every BI and AI initiative, offering lightning-fast analytics at an infinite scale with maximum cost efficiency and the lowest possible carbon footprint. The platform provides high-performance storage for both structured and unstructured data, ensuring trusted data for AI applications. It is built to scale seamlessly, making it an ideal solution for enterprises aiming to maximize their data’s potential. Kyvos is infrastructure-agnostic, which means it fits perfectly into any modern data or AI stack, whether deployed on-premises or in the cloud. Leading companies rely on Kyvos as a unified source for cost-effective, high-performance analytics that foster deep, meaningful insights and context-aware AI application development. By leveraging Kyvos, organizations can break through data barriers, accelerate decision-making, and enhance their AI-driven initiatives. The platform's flexibility allows businesses to create a scalable foundation for a range of data-driven solutions.

Evergreen is a next-generation cloud platform that supports a wide range of professional services, including AEC, accounting and marketing, legal, and consulting. Evergreen is built on Microsoft Dynamics 365 and Microsoft 365 and Microsoft Azure. It serves as a processing engine and transactional engine for core professional service functions such as project management, time billing, scheduling, onboarding, and billing. The solution provides more functionality than legacy ERP or Professional Services Automation (PSA) solutions. It helps to increase productivity and profitability by ensuring efficient project, accounting, financial and resource management. Evergreen is a true cloud. This means that you can control your cloud, your data, and the costs associated with it. You can also customize it and configure it to integrate with other services. sa.go! provides rapid value through a proven architecture, defined and tested processes, ready-to-use data models and report packs, advisory hours, and Managed Services. All this is packaged in an easy-to-consume, per-user per-month subscription price model which helps you scale your systems as your business grows.

Beye is an innovative generative business intelligence platform designed to rapidly process and cleanse raw data from various sources like spreadsheets, ERPs, and cloud applications, creating cohesive and AI-enhanced data environments in a matter of weeks instead of months. Its generative BI agent automatically constructs your initial data model and introductory dashboards tailored to your specific needs, utilizing metadata, semantic layers, and measures while eliminating the need for manual data preparation. Users at all levels, from business personnel to executives, can pose inquiries in straightforward English without requiring knowledge of SQL or navigating complex dashboards, gaining immediate access to detailed analytics, contextual insights, and explanations of underlying causes with verifiable queries. Furthermore, Beye integrates flawlessly with platforms such as SAP, Snowflake, Salesforce, and NetSuite, among more than 50 other data sources, enabling collaborative features and personalized metrics while ensuring the reliability of responses through AI-powered workflows. Ultimately, Beye empowers organizations to make data-driven decisions more efficiently and confidently.

IBM Engineering Lifecycle Management (ELM) serves as a cutting-edge, all-inclusive engineering solution, effectively guiding users through every stage from requirements gathering to systems design, workflow, and testing management, while enhancing the capabilities of ALM tools to support intricate systems development. By embracing a holistic perspective throughout the product lifecycle and establishing a robust digital infrastructure for data traceability, organizations can efficiently monitor changes, thereby mitigating risks and lowering expenses. Tackle the intricacies of engineering from conceptual design to final execution, foster collaboration among teams through a cohesive digital thread, utilize modeling and reuse strategies, capitalize on insights derived from automated reporting, and confidently scale operations to create a robust foundation for continuous innovation. The interlinked data within the digital thread will provide a unified source of truth, empowering functional, software, mechanical, and electrical engineering disciplines to work in harmony. This interconnected approach not only streamlines processes but also enhances the overall efficiency and effectiveness of the engineering lifecycle.

Aiimi’s Workplace AI platform serves as a comprehensive AI and data management solution designed for enterprises, seamlessly integrating all types of structured and unstructured data within an organization through a unified Virtual Data Layer. This integration facilitates secure and scalable AI-driven functionalities, including search, analysis, automation, and the derivation of actionable insights. By employing advanced technologies such as AI, machine learning, and Retrieval Augmented Generation (RAG), the platform effectively discovers, classifies, enriches, and governs data on a large scale, transforming disjointed information into reliable, “AI-ready” datasets. These datasets empower users with natural language search capabilities, contextual chat and assistant features, sophisticated Q&A functionalities, and visual representations such as knowledge graphs and timelines. Moreover, the platform automates intricate tasks related to data governance, compliance monitoring, enhancement of data quality, handling of DSAR/disclosures, and migration between cloud and legacy systems, all while ensuring the maintenance of access controls, permissions, and detailed audit trails. This comprehensive approach not only streamlines operations but also enhances data accessibility and usability across the enterprise.

LiveSource manages all aspects of the launch process, including Supplier Risk Management, Product Launch, and Collaborative Manufacturing. LiveSource is the first portal that allows product launches to be managed from one place. It ensures that all departments and stakeholders have access to the most current information. There are many other products that can cover parts of what we do but none that support the entire launch process and data. An ERP is not an option. Not a QMS. A PLM is not required. LiveSource is specifically designed to support direct material manufacturing. Indirect sourcing solutions are not able to handle the complex cost breakdowns and downstream processes required for highly engineered parts. LiveSource does this every day for 18,000 manufacturers. LiveSource connects your internal departments with your suppliers, managing the continuous changes during the launch. LiveSource streamlines, centralizes and documents the entire process.

Orbit is a sophisticated financial intelligence platform driven by artificial intelligence, designed to convert both unstructured and structured financial data into precise, transparent, and scalable insights. It merges a state-of-the-art AI Studio with an extensive repository containing millions of documents related to thousands of global companies, facilitating the automation of workflows and simplifying intricate research processes. By leveraging advanced AI models alongside pre-configured analytical tools, Orbit enhances areas such as competitor intelligence, due diligence, risk and compliance assessment, earnings analysis, investment research, and ESG evaluations, enabling in-depth analysis on a large scale. The platform’s flexible architecture features various components, including Orbit Insight for informed decision-making, AI-optimized knowledge bases for extensive financial content, and tools that efficiently standardize and manage diverse data inputs. Users benefit from capabilities such as semantic search and chat, automated data extraction, the creation of personalized knowledge bases, and the deployment of analytical agents, ensuring a comprehensive approach to financial analysis. Additionally, Orbit's design prioritizes user experience, making it accessible for professionals at all levels.

Dremio provides lightning-fast queries as well as a self-service semantic layer directly to your data lake storage. No data moving to proprietary data warehouses, and no cubes, aggregation tables, or extracts. Data architects have flexibility and control, while data consumers have self-service. Apache Arrow and Dremio technologies such as Data Reflections, Columnar Cloud Cache(C3), and Predictive Pipelining combine to make it easy to query your data lake storage. An abstraction layer allows IT to apply security and business meaning while allowing analysts and data scientists access data to explore it and create new virtual datasets. Dremio's semantic layers is an integrated searchable catalog that indexes all your metadata so business users can make sense of your data. The semantic layer is made up of virtual datasets and spaces, which are all searchable and indexed.

Openkoda is a specialized application development platform tailored specifically for the insurance industry. It enables carriers, insurtech companies, managing general agents (MGAs), startups, and service providers to efficiently create comprehensive web applications, APIs, and internal tools much quicker than traditional development methods allow, while also ensuring that teams maintain complete control over their source code. Built on an open-source framework, the platform eliminates the risk of vendor lock-in. With a modular architecture and pre-existing templates, Openkoda speeds up essential insurance processes such as policy issuance, claims handling, endorsements, and renewals, all within containerized environments that can scale horizontally and do not impose per-user fees. Additionally, Openkoda comes equipped with industry-specific accelerators, including an AI-capable policy administration core, claims management workbenches, embedded insurance checkout components, and dashboards for underwriters that can access external risk information or activate machine learning models. This robust functionality positions Openkoda as a powerful asset for any organization looking to innovate within the insurance landscape.

Identify your strengths while addressing concealed bottlenecks to enhance productivity and agility throughout your DevOps lifecycle. Gain practical insights to elevate efficiency in each phase by integrating data from Jira, Jenkins, GitHub, GitLab, Azure DevOps, SonarQube, and various other platforms. Utilize software metrics to assess agile velocity, quality, security, and data integrity. Create customizable dashboards that allow you to aggregate or examine details as needed. Accelerate the development of high-quality products and focus on delivering outcomes that prioritize customer needs. Enhance data integrity and streamline processes to drive efficiency. Foster a culture that values collaboration and recognition, which can lead to improved employee retention rates. Regularly evaluate the quality of requirements, acceptance criteria, and agile sprint plans to ensure clarity and precision. Implement automation for issue routing and task reminders to minimize downtime and unproductive waiting periods. Stay ahead of potential risks with early warnings regarding sprint delays. Make informed decisions by considering customer impact, and use timely alerts to speed up pull request reviews and merges, thus enhancing overall velocity. Additionally, conditionally automate repetitive task sequences to alleviate cognitive burden and maintain team focus on more critical tasks. Strive for continuous improvement by consistently analyzing and refining your processes.

Edka streamlines the establishment of a production-ready Platform as a Service (PaaS) using standard cloud virtual machines and Kubernetes, significantly minimizing the manual labor needed to manage applications on Kubernetes by offering preconfigured open-source add-ons that effectively transform a Kubernetes cluster into a comprehensive PaaS solution. To enhance Kubernetes operations, Edka organizes them into distinct layers: Layer 1: Cluster provisioning – A user-friendly interface that allows for the effortless creation of a k3s-based cluster with just one click and default settings. Layer 2: Add-ons – A convenient one-click deployment option for essential components like metrics-server, cert-manager, and various operators, all preconfigured for use with Hetzner, requiring no additional setup. Layer 3: Applications – User interfaces with minimal configurations tailored for applications that utilize the aforementioned add-ons. Layer 4: Deployments – Edka ensures automatic updates to deployments in accordance with semantic versioning rules, offering features such as instant rollbacks, autoscaling capabilities, persistent volume management, secret/environment imports, and quick public accessibility for applications. Furthermore, this structure allows developers to focus on building their applications rather than managing the underlying infrastructure.

Enrich Layer serves as a B2B data enrichment platform featuring a unified API designed to equip developers and technical teams with thorough, current intelligence on individuals, organizations, job listings, and online signals, enabling applications and internal systems to enhance user and prospect information without the hassle of managing several vendors. It consolidates and standardizes public and structured data into a reliable RESTful JSON API, allowing users to retrieve in-depth professional profiles that include work history, educational background, skill sets, contact information, firmographics like revenue and industry, as well as dynamic market indicators such as active job openings and SEO trends. This functionality supports various applications such as CRM enrichment, lead scoring, recruitment tools, sales intelligence, and analytical products. Moreover, Enrich Layer amalgamates multiple curated data sources into a singular layer that scales efficiently with usage, while also offering robust query capabilities across people, company, and job datasets through bulk endpoints and webhooks. With these features, teams can significantly automate and streamline their data enrichment workflows, enhancing overall operational efficiency.

OpenText ALM Quality Center is a single-pane-of-glass for software quality management. It helps you implement rigorous and auditable lifecycle process and govern application lifecycle activities. Use the reusability and traceability of ALM tools, as well as their automation, to improve efficiency when developing, testing and maintaining applications. Automate the collection of results and gain real-time insight into application quality by eliminating time-consuming, error-prone manual processes. Standardize processes with templates and workflows built into ALM. Ensure compliance through detailed change tracking, a risk-based strategy and detailed change tracking. Integrate and use APIs to create a vendor-neutral, automated ecosystem for data consolidation throughout your application lifecycle. Choose the deployment option that best meets your needs for scalability and security.