Alternatives to PwC Model Edge

Gemini Enterprise Agent Platform is Google Cloud’s next-generation system for designing and managing advanced AI agents across the enterprise. Built as the successor to Vertex AI, it unifies model selection, development, and deployment into a single scalable environment. The platform supports a vast ecosystem of over 200 AI models, including Google’s latest Gemini innovations and popular third-party models. It offers flexible development tools like Agent Studio for visual workflows and the Agent Development Kit for deeper customization. Businesses can deploy agents that operate continuously, maintain long-term memory, and handle multi-step processes with high efficiency. Security and governance are central, with features such as agent identity verification, centralized registries, and controlled access through gateways. The platform also enables seamless integration with enterprise systems, allowing agents to interact with data, applications, and workflows securely. Advanced monitoring tools provide real-time insights into agent behavior and performance. Optimization features help refine agent logic and improve accuracy over time. By combining automation, intelligence, and governance, the platform helps organizations transition to autonomous, AI-driven operations. It ultimately supports faster innovation while maintaining enterprise-grade reliability and control.

Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.

Risk Cloud™, LogicGate's most popular GRC process automation platform Risk Cloud™, allows organizations to transform disorganized compliance and risk operations into agile process apps without having to write a single line code. LogicGate believes that enterprise technology can make a significant difference in the lives of employees and their organizations. We aim to transform the way companies manage governance, risk, compliance (GRC), programs so that they can manage risk with confidence. LogicGate's Risk Cloud platform, cloud-based applications, and raving fan service, combined with expertly crafted content, allow organizations to transform disorganized compliance operations into agile processes without writing a line of code.

Docker streamlines tedious configuration processes and is utilized across the entire development lifecycle, facilitating swift, simple, and portable application creation on both desktop and cloud platforms. Its all-encompassing platform features user interfaces, command-line tools, application programming interfaces, and security measures designed to function cohesively throughout the application delivery process. Jumpstart your programming efforts by utilizing Docker images to craft your own distinct applications on both Windows and Mac systems. With Docker Compose, you can build multi-container applications effortlessly. Furthermore, it seamlessly integrates with tools you already use in your development workflow, such as VS Code, CircleCI, and GitHub. You can package your applications as portable container images, ensuring they operate uniformly across various environments, from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE, and beyond. Additionally, Docker provides access to trusted content, including official Docker images and those from verified publishers, ensuring quality and reliability in your application development journey. This versatility and integration make Docker an invaluable asset for developers aiming to enhance their productivity and efficiency.

TensorFlow is a comprehensive open-source machine learning platform that covers the entire process from development to deployment. This platform boasts a rich and adaptable ecosystem featuring various tools, libraries, and community resources, empowering researchers to advance the field of machine learning while allowing developers to create and implement ML-powered applications with ease. With intuitive high-level APIs like Keras and support for eager execution, users can effortlessly build and refine ML models, facilitating quick iterations and simplifying debugging. The flexibility of TensorFlow allows for seamless training and deployment of models across various environments, whether in the cloud, on-premises, within browsers, or directly on devices, regardless of the programming language utilized. Its straightforward and versatile architecture supports the transformation of innovative ideas into practical code, enabling the development of cutting-edge models that can be published swiftly. Overall, TensorFlow provides a powerful framework that encourages experimentation and accelerates the machine learning process.

Enhance the efficiency of your model lifecycle confidently by utilizing our AI-powered model risk management technology, the Chiron MRM Platform, which automates real-time model validation and monitoring. The process of validating models can often be both time-consuming and expensive. However, with our enterprise risk management solutions, clients can decrease validation costs by up to tenfold. Chiron's advanced monitoring features facilitate the early identification of potential model failures, which contributes to the development of superior models and diminished capital requirements. To ensure that models can be relied upon for decision-making, it is essential to maintain a clear and auditable perspective on the models implemented in your organization. Chiron Enterprise provides a tailored model inventory that tracks all models throughout their lifecycle, alongside a flexible workflow engine designed to optimize processes. By scaling your model risk operations, you can enforce organized and consistent workflows across your teams, ultimately driving better outcomes for your organization. Moreover, this comprehensive approach supports a culture of accountability and continuous improvement in model management.

Focus is a central tool that improves model governance transparency, efficiency, effectiveness, and transparency. Focus helps you to adhere to the best practices for regulatory requirements in a controlled and systematic way. To ensure you are on the right track, define and follow policy and processes. Keep detailed records, report and remediate to ensure that you do not forget. It allows for easy, controlled access of all models, reports, documents, and up-to-the minute status, tasks, and actions dashboards. This facilitates better prioritization, resource planning, and a single, practical solution. - Model dependencies, taxonomy and defined data - Centralised model inventory - Model risks identified and remediation plans tracked - Model lifecycle events, workflow management - Full audit trail, tracking, and reporting - User configurable reporting/querying - Flexibility in implementation

Both AI and non-AI models require effective risk management and oversight to function optimally. Fairly offers a continuous monitoring system designed for robust model governance and oversight. This platform facilitates seamless collaboration between risk and compliance teams alongside data science and cyber security professionals, ensuring that models maintain reliability and security standards. Fairly provides a straightforward approach to staying current with policies and regulations related to the procurement, validation, and auditing of non-AI, predictive AI, and generative AI models. The model validation and auditing process is streamlined by Fairly, which grants direct access to ground truth in a controlled environment for both in-house and third-party models, all while minimizing additional burdens on development and IT teams. This ensures that Fairly's platform not only promotes compliance but also fosters secure and ethical modeling practices. Furthermore, Fairly empowers teams to effectively identify, assess, and monitor risks while also reporting and mitigating compliance, operational, and model-related risks in alignment with both internal policies and external regulations. By incorporating these features, Fairly reinforces its commitment to maintaining high standards of model integrity and accountability.

ValidMind stands out as the premier solution for organizations seeking to streamline the automation of testing, documentation, and risk management concerning AI and statistical models. This comprehensive platform offers a range of tools designed to assist data scientists, corporations, and risk or compliance professionals in pinpointing and documenting potential risks linked to their AI models, ensuring adherence to regulatory standards. With its integrated features, ValidMind simplifies the review process of risk areas across various teams' models, allowing organizations to effectively prioritize compliance and risk mitigation efforts. Furthermore, ValidMind promotes collaboration by breaking down information silos, thereby alleviating the complexities involved in sharing and working together on model documentation, validation reports, and risk assessments throughout the entire model lifecycle. By leveraging ValidMind, organizations can foster a culture of transparency and accountability in their AI practices.

Your program is just one vulnerability away from overlooking significant risks. As financial institutions increasingly integrate complex calculations, holistic business models, and designated model owners, risk models are evolving in sophistication. Hidden dangers may lurk within the gaps between different models. However, organizations need not endure the fragmentation of their programs. The Crowe Model Risk Manager offers a software solution that seamlessly integrates model risk management throughout the entire process. This centralized platform, equipped with real-time visualizations, simplifies the management of workflows, allows for effective issue tracking, report generation, and compliance demonstration. Financial institutions can transition beyond traditional spreadsheets and emails to achieve a unified and thorough perspective. With our innovative software, understanding all facets of your model risk management becomes straightforward and accessible. Model owners can easily identify their responsibilities, along with defined next steps and continuous activity oversight. Furthermore, banks can implement automated workflows and actions, enhancing efficiency and ensuring the program remains on track. This holistic approach empowers organizations to proactively address risks, ultimately leading to more robust risk management strategies.

The Modelscape solution streamlines the management of financial models' lifecycle for financial institutions, enhancing documentation, transparency, and compliance. By adopting this solution across the entire model lifecycle, users can take advantage of standardized workflows, automated documentation processes, and seamless artifact linking. This approach allows for the horizontal and vertical scaling of algorithms, models, and applications. Additionally, it supports various enterprise infrastructures and programming languages, including Python, R, SAS, and MATLAB. Comprehensive tracking of issues throughout the model lifecycle is facilitated by full model lineage and detailed reporting on issues and usage. An executive dashboard provides insights into model data, enables custom algorithm execution, and offers automated workflows, all while granting web-based access to a thorough, auditable inventory of models and their dependencies. Users can also develop, back-test, and document their models and methodologies effectively. This solution significantly enhances the transparency, reproducibility, and reusability of financial models, while also automatically generating the necessary documentation and reports to support ongoing compliance efforts. In doing so, it empowers financial institutions to maintain high standards in model governance and operational efficiency.

Mistral AI’s Forge is a powerful enterprise AI platform designed to help organizations build highly specialized models using their own proprietary data and knowledge systems. It offers a comprehensive pipeline that spans pre-training, synthetic data generation, reinforcement learning, evaluation, and deployment. Businesses can customize models by incorporating internal datasets, ontologies, and workflows, ensuring outputs are aligned with real operational needs. Forge supports advanced techniques such as RLHF, LoRA, and supervised fine-tuning to refine model behavior and performance efficiently. The platform includes robust evaluation frameworks that focus on enterprise KPIs, enabling organizations to measure real-world impact rather than relying on standard benchmarks. With flexible infrastructure options, companies can deploy models across private cloud, on-premises environments, or Mistral’s compute layer without vendor lock-in. Forge also provides lifecycle management tools to track model versions, datasets, and training configurations with full traceability. Its synthetic data generation capabilities allow teams to create high-quality training examples, including rare edge cases and compliance-specific scenarios. Security and governance are built into every stage, with strict data isolation and auditable workflows. Overall, Forge empowers enterprises to turn their internal knowledge into scalable, production-grade AI systems.

JFrog ML (formerly Qwak) is a comprehensive MLOps platform that provides end-to-end management for building, training, and deploying AI models. The platform supports large-scale AI applications, including LLMs, and offers capabilities like automatic model retraining, real-time performance monitoring, and scalable deployment options. It also provides a centralized feature store for managing the entire feature lifecycle, as well as tools for ingesting, processing, and transforming data from multiple sources. JFrog ML is built to enable fast experimentation, collaboration, and deployment across various AI and ML use cases, making it an ideal platform for organizations looking to streamline their AI workflows.

Models may be fleeting, but pipelines have a lasting presence. The cycle of training, evaluating, deploying, and repeating is essential. Valohai stands out as the sole MLOps platform that fully automates the entire process, from data extraction right through to model deployment. Streamline every aspect of this journey, ensuring that every model, experiment, and artifact is stored automatically. You can deploy and oversee models within a managed Kubernetes environment. Simply direct Valohai to your code and data, then initiate the process with a click. The platform autonomously launches workers, executes your experiments, and subsequently shuts down the instances, relieving you of those tasks. You can work seamlessly through notebooks, scripts, or collaborative git projects using any programming language or framework you prefer. The possibilities for expansion are limitless, thanks to our open API. Each experiment is tracked automatically, allowing for easy tracing from inference back to the original data used for training, ensuring full auditability and shareability of your work. This makes it easier than ever to collaborate and innovate effectively.

Azure Machine Learning Studio enables organizations to streamline the entire machine learning lifecycle from start to finish. Equip developers and data scientists with an extensive array of efficient tools for swiftly building, training, and deploying machine learning models. Enhance the speed of market readiness and promote collaboration among teams through leading-edge MLOps—akin to DevOps but tailored for machine learning. Drive innovation within a secure, reliable platform that prioritizes responsible AI practices. Cater to users of all expertise levels with options for both code-centric and drag-and-drop interfaces, along with automated machine learning features. Implement comprehensive MLOps functionalities that seamlessly align with existing DevOps workflows, facilitating the management of the entire machine learning lifecycle. Emphasize responsible AI by providing insights into model interpretability and fairness, securing data through differential privacy and confidential computing, and maintaining control over the machine learning lifecycle with audit trails and datasheets. Additionally, ensure exceptional compatibility with top open-source frameworks and programming languages such as MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R, thus broadening accessibility and usability for diverse projects. By fostering an environment that promotes collaboration and innovation, teams can achieve remarkable advancements in their machine learning endeavors.

MLflow is an open-source suite designed to oversee the machine learning lifecycle, encompassing aspects such as experimentation, reproducibility, deployment, and a centralized model registry. The platform features four main components that facilitate various tasks: tracking and querying experiments encompassing code, data, configurations, and outcomes; packaging data science code to ensure reproducibility across multiple platforms; deploying machine learning models across various serving environments; and storing, annotating, discovering, and managing models in a unified repository. Among these, the MLflow Tracking component provides both an API and a user interface for logging essential aspects like parameters, code versions, metrics, and output files generated during the execution of machine learning tasks, enabling later visualization of results. It allows for logging and querying experiments through several interfaces, including Python, REST, R API, and Java API. Furthermore, an MLflow Project is a structured format for organizing data science code, ensuring it can be reused and reproduced easily, with a focus on established conventions. Additionally, the Projects component comes equipped with an API and command-line tools specifically designed for executing these projects effectively. Overall, MLflow streamlines the management of machine learning workflows, making it easier for teams to collaborate and iterate on their models.

Connected Risk provides your team with a comprehensive solution to meet all governance, risk, and compliance (GRC) requirements in a unified platform. Built on our innovative, low-code/no-code framework, EmpoweredNEXT, Connected Risk’s robust infrastructure allows for the customization of applications tailored specifically to the needs of your team. This integrated approach to holistic risk management is crafted to oversee your governance, risk, and compliance programs throughout a cohesive lifecycle that caters to your organization’s unique demands. Trusted by leading global entities daily, it serves as a reliable tool for addressing GRC requirements. Additionally, enterprise risk management equips your organization with essential tools to navigate both risks and disruptions effectively. Furthermore, regulatory change management empowers your compliance team to handle changes in a structured and interconnected way. Lastly, model risk management enables your organization to develop and sustain an efficient model inventory through effective workflow oversight.

Enhance your organization's machine learning capabilities through seamless management, deployment, and monitoring on a premier AI platform, all powered by navio. This tool enables the execution of a wide range of machine learning operations throughout your entire AI ecosystem. Transition your experiments from the lab to real-world applications, seamlessly incorporating machine learning into your operations for tangible business results. Navio supports you at every stage of the model development journey, from initial creation to deployment in a production environment. With automatic REST endpoint generation, you can easily monitor interactions with your model across different users and systems. Concentrate on exploring and fine-tuning your models to achieve optimal outcomes, while navio streamlines the setup of infrastructure and auxiliary features, saving you valuable time and resources. By allowing navio to manage the entire process of operationalizing your models, you can rapidly bring your machine learning innovations to market and start realizing their potential impact. This approach not only enhances efficiency but also boosts your organization's overall productivity in leveraging AI technologies.

The NeoPulse Product Suite offers a comprehensive solution for businesses aiming to develop tailored AI applications utilizing their own selected data. It features a robust server application equipped with a powerful AI known as “the oracle,” which streamlines the creation of advanced AI models through automation. This suite not only oversees your AI infrastructure but also coordinates workflows to facilitate AI generation tasks seamlessly. Moreover, it comes with a licensing program that empowers any enterprise application to interact with the AI model via a web-based (REST) API. NeoPulse stands as a fully automated AI platform that supports organizations in training, deploying, and managing AI solutions across diverse environments and at scale. In essence, NeoPulse can efficiently manage each stage of the AI engineering process, including design, training, deployment, management, and eventual retirement, ensuring a holistic approach to AI development. Consequently, this platform significantly enhances the productivity and effectiveness of AI initiatives within an organization.

Entry Point AI serves as a cutting-edge platform for optimizing both proprietary and open-source language models. It allows users to manage prompts, fine-tune models, and evaluate their performance all from a single interface. Once you hit the ceiling of what prompt engineering can achieve, transitioning to model fine-tuning becomes essential, and our platform simplifies this process. Rather than instructing a model on how to act, fine-tuning teaches it desired behaviors. This process works in tandem with prompt engineering and retrieval-augmented generation (RAG), enabling users to fully harness the capabilities of AI models. Through fine-tuning, you can enhance the quality of your prompts significantly. Consider it an advanced version of few-shot learning where key examples are integrated directly into the model. For more straightforward tasks, you have the option to train a lighter model that can match or exceed the performance of a more complex one, leading to reduced latency and cost. Additionally, you can configure your model to avoid certain responses for safety reasons, which helps safeguard your brand and ensures proper formatting. By incorporating examples into your dataset, you can also address edge cases and guide the behavior of the model, ensuring it meets your specific requirements effectively. This comprehensive approach ensures that you not only optimize performance but also maintain control over the model's responses.

Datatron provides tools and features that are built from scratch to help you make machine learning in production a reality. Many teams realize that there is more to deploying models than just the manual task. Datatron provides a single platform that manages all your ML, AI and Data Science models in production. We can help you automate, optimize and accelerate your ML model production to ensure they run smoothly and efficiently. Data Scientists can use a variety frameworks to create the best models. We support any framework you use to build a model (e.g. TensorFlow and H2O, Scikit-Learn and SAS are supported. Explore models that were created and uploaded by your data scientists, all from one central repository. In just a few clicks, you can create scalable model deployments. You can deploy models using any language or framework. Your model performance will help you make better decisions.

The SageMaker Edge Agent enables the collection of data and metadata triggered by your specifications, facilitating the retraining of current models with real-world inputs or the development of new ones. This gathered information can also serve to perform various analyses, including assessments of model drift. There are three deployment options available to cater to different needs. GGv2, which is approximately 100MB in size, serves as a fully integrated AWS IoT deployment solution. For users with limited device capabilities, a more compact built-in deployment option is offered within SageMaker Edge. Additionally, for clients who prefer to utilize their own deployment methods, we accommodate third-party solutions that can easily integrate into our user workflow. Furthermore, Amazon SageMaker Edge Manager includes a dashboard that provides insights into the performance of models deployed on each device within your fleet. This dashboard not only aids in understanding the overall health of the fleet but also assists in pinpointing models that may be underperforming, ensuring that you can take targeted actions to optimize performance. By leveraging these tools, users can enhance their machine learning operations effectively.

Although not every model possesses the same quality, it is crucial for all models to have governance in place to promote responsible and ethical decision-making within an organization. The IBM® watsonx.governance™ toolkit for AI governance empowers you to oversee, manage, and track your organization's AI initiatives effectively. By utilizing software automation, it enhances your capacity to address risks, fulfill regulatory obligations, and tackle ethical issues related to both generative AI and machine learning (ML) models. This toolkit provides access to automated and scalable governance, risk, and compliance instruments that encompass aspects such as operational risk, policy management, compliance, financial oversight, IT governance, and both internal and external audits. You can proactively identify and mitigate model risks while converting AI regulations into actionable policies that can be enforced automatically, ensuring that your organization remains compliant and ethically sound in its AI endeavors. Furthermore, this comprehensive approach not only safeguards your operations but also fosters trust among stakeholders in the integrity of your AI systems.

Koog is a Kotlin-based framework designed for developing and executing AI agents using idiomatic Kotlin, catering to both simple agents that handle individual inputs and more intricate workflow agents with tailored strategies and configurations. Its architecture is built entirely in Kotlin, ensuring a smooth integration of the Model Control Protocol (MCP) for improved management of models. The framework also utilizes vector embeddings to facilitate semantic search and offers a versatile system for creating and enhancing tools that can interact with external systems and APIs. Components that are ready for immediate use tackle prevalent challenges in AI engineering, while intelligent history compression techniques are employed to optimize token consumption and maintain context. Additionally, a robust streaming API supports real-time response processing and allows for simultaneous tool invocations. Agents benefit from persistent memory, which enables them to retain knowledge across different sessions and among various agents, and detailed tracing facilities enhance the debugging and monitoring process, ensuring developers have the insights needed for effective optimization. This combination of features positions Koog as a comprehensive solution for developers looking to harness the power of AI in their applications.

LMOps is a stack that allows you to launch production-ready applications for monitoring, model management and more. Portkey is a replacement for OpenAI or any other provider APIs. Portkey allows you to manage engines, parameters and versions. Switch, upgrade, and test models with confidence. View aggregate metrics for your app and users to optimize usage and API costs Protect your user data from malicious attacks and accidental exposure. Receive proactive alerts if things go wrong. Test your models in real-world conditions and deploy the best performers. We have been building apps on top of LLM's APIs for over 2 1/2 years. While building a PoC only took a weekend, bringing it to production and managing it was a hassle! We built Portkey to help you successfully deploy large language models APIs into your applications. We're happy to help you, regardless of whether or not you try Portkey!

The EY Trusted AI Platform offers organizations valuable insights into the origins and factors contributing to risk, while also assisting an AI design team in assessing and quantifying these risks. Utilizing interactive, web-based tools for schematics and assessments, the platform constructs a detailed risk profile for an AI system. It employs a sophisticated analytical model that transforms user inputs into a composite score, which reflects the technical risk, stakeholder impact, and control effectiveness associated with the AI system. To evaluate technical risk, the platform analyzes the AI system's design, focusing on various risk drivers such as the technologies utilized, the operating environment, and the system’s degree of autonomy. Additionally, when assessing stakeholder risk, the platform takes into account the goals and objectives set for the AI system, as well as the financial, emotional, and physical repercussions for both internal and external users, along with potential reputational, regulatory, and legal challenges that may arise. Overall, this comprehensive approach ensures that all facets of risk are thoroughly examined and understood.

Sagify enhances AWS Sagemaker by abstracting its intricate details, allowing you to devote your full attention to Machine Learning. While Sagemaker serves as the core ML engine, Sagify provides a user-friendly interface tailored for data scientists. By simply implementing two functions—train and predict—you can efficiently train, fine-tune, and deploy numerous ML models. This streamlined approach enables you to manage all your ML models from a single platform, eliminating the hassle of low-level engineering tasks. With Sagify, you can say goodbye to unreliable ML pipelines, as it guarantees consistent training and deployment on AWS. Thus, by focusing on just two functions, you gain the ability to handle hundreds of ML models effortlessly.

Data science projects achieve success when data scientists possess the ability to independently create, enhance, and manage comprehensive workflows while prioritizing their data science tasks over engineering concerns. By utilizing Metaflow alongside popular data science libraries like TensorFlow or SciKit Learn, you can write your models in straightforward Python syntax without needing to learn much that is new. Additionally, Metaflow supports the R programming language, broadening its usability. This tool aids in designing workflows, scaling them effectively, and deploying them into production environments. It automatically versions and tracks all experiments and data, facilitating easy inspection of results within notebooks. With tutorials included, newcomers can quickly familiarize themselves with the platform. You even have the option to duplicate all tutorials right into your current directory using the Metaflow command line interface, making it a seamless process to get started and explore further. As a result, Metaflow not only simplifies complex tasks but also empowers data scientists to focus on impactful analyses.

Data Version Control (DVC) is an open-source system specifically designed for managing version control in data science and machine learning initiatives. It provides a Git-like interface that allows users to systematically organize data, models, and experiments, making it easier to oversee and version various types of files such as images, audio, video, and text. This system helps structure the machine learning modeling process into a reproducible workflow, ensuring consistency in experimentation. DVC's integration with existing software engineering tools is seamless, empowering teams to articulate every facet of their machine learning projects through human-readable metafiles that detail data and model versions, pipelines, and experiments. This methodology promotes adherence to best practices and the use of well-established engineering tools, thus bridging the gap between the realms of data science and software development. By utilizing Git, DVC facilitates the versioning and sharing of complete machine learning projects, encompassing source code, configurations, parameters, metrics, data assets, and processes by committing the DVC metafiles as placeholders. Furthermore, its user-friendly approach encourages collaboration among team members, enhancing productivity and innovation within projects.

Regardless of how a financial institution assesses risk, SAS offers established methodologies and optimal practices that aid in cultivating a culture focused on risk awareness, enhancing capital and liquidity management, and fulfilling regulatory requirements. By empowering your risk management team with on-demand, high-performance analytics, you can achieve improved efficiency and transparency. It's crucial to strike a harmonious balance between immediate and future strategies while confidently navigating evolving regulatory landscapes. SAS provides a diverse array of scalable credit models designed to actively manage loan portfolios, ensuring enhanced regulatory compliance and robust balance sheet management capabilities. Additionally, you can conduct simulations across various scenarios, yielding quicker results with in-depth analyses that support informed business decision-making, ultimately leading to stronger financial outcomes. This proactive approach not only mitigates risks but also positions institutions to thrive in a complex financial environment.

Apparity is a robust platform that streamlines the management of end user computing (EUC) risks, complemented by exceptional customer support. It effectively identifies, catalogs, evaluates, and oversees the end user applications that are essential for your key business operations, covering a wide range of tools such as spreadsheets, models, databases, coding scripts, and business intelligence software. Our platform enhances visibility across the enterprise by providing a thorough audit of all EUC-related activities. How is this accomplished? By utilizing precise file tracking and version control, you can efficiently oversee your EUC inventory while ensuring adherence to regulatory standards. Once implemented, users will experience improved collaboration and heightened process automation, which ultimately leads to greater operational efficiency.

Gate22 serves as a robust AI governance and Model Context Protocol (MCP) control platform designed for enterprises, centralizing the security and oversight of how AI tools and agents interact with MCP servers within an organization. It empowers administrators to onboard, configure, and regulate both internal and external MCP servers, offering detailed permissions at the functional level, team-based access control, and role-specific policies to ensure that only sanctioned tools and functionalities are accessible to designated teams or users. By providing a cohesive MCP endpoint, Gate22 aggregates multiple MCP servers into an intuitive interface featuring just two primary functions, leading to reduced token consumption for developers and AI clients, while effectively minimizing context overload and ensuring both precision and security. The administrative interface includes a governance dashboard that allows for the monitoring of usage trends, compliance maintenance, and enforcement of least-privilege access, while the member interface facilitates streamlined and secure access to authorized MCP bundles. This dual-view approach not only enhances operational efficiency but also strengthens overall security within the organizational framework.

CIMCON Software addresses the operational risks associated with end-user computing (EUC) files, which encompass issues like regulatory reporting inaccuracies, non-compliance, cyber threats, and fraud. These risks arise from various EUCs, including spreadsheets, models, Access databases, applications coded in languages such as VBScript, R, and Python, as well as self-service analytics platforms like Tableau and QlikView. Financial institutions heavily depend on EUC tools, such as Excel spreadsheets and scripts, to adapt swiftly to evolving market demands and regulatory changes. These applications are crucial for tasks ranging from financial modeling to accounting and ensuring adherence to regulatory standards, necessitating effective management. To aid in this, CIMCON Software provides solutions that compile a comprehensive inventory of all EUCs within an organization, pinpoint the most vital files, identify errors, visualize data dependencies, and ensure continuous monitoring and control of critical EUCs. By streamlining this process, organizations can significantly mitigate risks and enhance their operational efficiency.

The Kubeflow initiative aims to simplify the process of deploying machine learning workflows on Kubernetes, ensuring they are both portable and scalable. Rather than duplicating existing services, our focus is on offering an easy-to-use platform for implementing top-tier open-source ML systems across various infrastructures. Kubeflow is designed to operate seamlessly wherever Kubernetes is running. It features a specialized TensorFlow training job operator that facilitates the training of machine learning models, particularly excelling in managing distributed TensorFlow training tasks. Users can fine-tune the training controller to utilize either CPUs or GPUs, adapting it to different cluster configurations. In addition, Kubeflow provides functionalities to create and oversee interactive Jupyter notebooks, allowing for tailored deployments and resource allocation specific to data science tasks. You can test and refine your workflows locally before transitioning them to a cloud environment whenever you are prepared. This flexibility empowers data scientists to iterate efficiently, ensuring that their models are robust and ready for production.

See the risks. Seize the opportunities. While others fear risk, we embrace it. For over 20 years, Protecht has redefined the way people think about risk management. We help companies increase performance and achieve strategic objectives by enabling you to better understand, monitor and manage risk. Protecht ERM is a single, integrated no-code SaaS platform that provides you with all the tools you need to dynamically manage all aspects of enterprise risk management and GRC. That includes risk assessments, key risk indicators (KRIs) and key performance indicators (KPIs), compliance, incidents, vendor and cyber/IT risk, operational resilience and business continuity, internal audit, and so much more. We’re with you for your full risk journey. Let’s transform the way you understand your risk appetite and manage your risk portfolio to create exciting opportunities for growth for your organization. Founded in 1999, Protecht is a leading provider of complete, cutting-edge and cost-effective enterprise risk management software, training and advisory solution, with headquarters in Sydney and offices in London and Los Angeles.

LogicManager is a powerful, holistic Enterprise Risk Management (ERM) platform built to unify governance, risk, and compliance efforts across your entire organization. Designed for risk professionals, compliance officers, internal auditors, and business leaders, LogicManager provides the structure, intelligence, and automation needed to turn risk into a strategic advantage. At its core is our patented Risk Ripple® Intelligence, which maps relationships between risks, controls, processes, vendors, and policies—so you can see how everything is connected. This gives you a dynamic, real-time view of your risk landscape and allows you to act proactively rather than reactively. Whether you're monitoring operational risks, managing regulatory compliance, conducting audits, or ensuring vendor due diligence, LogicManager empowers you to do it all from one centralized platform. Unlike point solutions or spreadsheets, LogicManager offers no-code configuration, robust workflow automation, and integrated tools for incident management, control testing, policy management, and strategic risk assessments. With LogicManager Expert (LMX)—our embedded AI assistant—you’ll receive best-practice recommendations, uncover hidden threats, and accelerate time to value with less manual effort. Trusted by organizations in healthcare, finance, government, education, and beyond, LogicManager simplifies complex processes, improves accountability, and provides board-ready reporting that proves the effectiveness of your governance strategy. Our flat-fee pricing and award-winning support ensure transparency and satisfaction at every step.

Citrusˣ offers a comprehensive platform focused on AI transparency and explainability, empowering organizations to uphold trust in their models. Through the web UI and SDK, data scientists can access Summary and Validation pages to evaluate their models' performance, analyze outcomes, and troubleshoot any issues that arise. Meanwhile, data science managers and Chief Data Officers can oversee their teams' progress, benchmark different models, and confirm that key performance indicators (KPIs) are being achieved. Risk officers and Model Risk Managers (MRMs) can utilize the web interface and detailed reports to ensure the models' reliability, evaluate associated risks, and confirm that AI is employed in a responsible and equitable manner in accordance with regulatory standards. Additionally, executives and regulatory bodies can leverage tailored summary reports to assess the robustness and precision of the models, comprehend the rationale behind their decisions, pinpoint potential risks, and guarantee adherence to compliance protocols, ultimately safeguarding the organization against legal repercussions and preserving its reputation in the industry. This multi-faceted approach ensures that all stakeholders are informed and engaged in the AI governance process.

Gesund stands as the pioneering compliant AI factory dedicated to facilitating the introduction of clinical-grade AI solutions into the market. In order to meet regulatory standards, our platform meticulously audits and validates third-party medical AI solutions, ensuring their safety, effectiveness, and fairness. Gesund seamlessly manages the entire AI/ML lifecycle for all participants by integrating models, data, and expertise within a user-friendly no-code environment. We offer standardized, cohesive, and diverse data tailored to meet your machine learning requirements and regulatory obligations. By evaluating the validation needs of models, Gesund.ai supplies an optimal combination of high-quality data sourced from its extensive network of clinical partners. Model owners can share their clinical studies with Gesund.ai to curate the necessary datasets, subsequently uploading their models onto Gesund.ai's federated validation platform, which can be situated on hospital premises or within a private cloud. Each model undergoes evaluation against a validation dataset that has been specifically curated on the hospital side, ensuring that the results are relevant and reliable, ultimately enhancing the quality of healthcare solutions. Through this comprehensive approach, Gesund not only supports compliance but also accelerates the path to effective AI deployment in clinical settings.

AI Cloud represents an innovative strategy designed to meet the current demands, challenges, and potential of artificial intelligence. This comprehensive system acts as a single source of truth, expediting the process of bringing AI solutions into production for organizations of all sizes. Users benefit from a collaborative environment tailored for ongoing enhancements throughout the entire AI lifecycle. The AI Catalog simplifies the process of discovering, sharing, tagging, and reusing data, which accelerates deployment and fosters teamwork. This catalog ensures that users can easily access relevant data to resolve business issues while maintaining high standards of security, compliance, and consistency. If your database is subject to a network policy restricting access to specific IP addresses, please reach out to Support for assistance in obtaining a list of IPs that should be added to your network policy for whitelisting, ensuring that your operations run smoothly. Additionally, leveraging AI Cloud can significantly improve your organization’s ability to innovate and adapt in a rapidly evolving technological landscape.

Eclipse Dirigible™ is an application platform designed for high productivity, offering both development tools and a runtime environment. It facilitates the complete development lifecycle of applications by utilizing an in-system programming model alongside rapid application development methods. This platform encompasses the entire development process, including database management and modeling, the creation of RESTful services with various dynamic languages, and user interface generation based on established patterns, as well as role-based security, integration of external services, testing, debugging, operations, and monitoring. All source code and example applications from the Eclipse Dirigible project are available under the Eclipse Public License v 2.0 and can be found on GitHub. Students can utilize this platform to work on projects, explore different technologies and scenarios, and learn widely-used programming languages. With Eclipse Dirigible, you have all the essential tools and resources at your disposal for any development venture you embark upon. Its comprehensive features make it an ideal choice for both beginners and experienced developers alike.

Tackle complex policy and procedure obstacles using Mitratech's PolicyHub, a comprehensive platform for managing policies. Efficient and budget-friendly, PolicyHub includes capabilities like policy oversight, automated evaluations of knowledge, auditing functions, and detailed reporting. This tool empowers organizations to showcase their commitment to corporate accountability and maintain a robust compliance program. Additionally, PolicyHub allows users to generate in-depth reports on the fly and swiftly respond to audits or investigations, ensuring that they stay ahead in a dynamic regulatory landscape. Ultimately, having such a solution in place can significantly enhance an organization's operational effectiveness and risk management strategies.

alvaModel is an advanced software application designed for the construction, validation, comparison, and implementation of QSAR and QSPR models. It excels in supporting both regression and classification tasks through the use of molecular descriptors and fingerprints, emphasizing transparency, interpretability, and scientific rigor in its models. This software offers a variety of data splitting techniques, variable selection approaches, and modeling algorithms, as well as thorough internal and external validation methods. Additionally, alvaModel includes diagnostic visualizations, applicability domain evaluations, and tools for model comparison, which aid users in pinpointing reliable and predictive modeling solutions. Crafted in accordance with the highest standards of chemometrics, alvaModel promotes the creation of interpretable models that align with OECD guidelines for QSAR validation, making it ideal for both research and regulatory uses. Its user-friendly graphical interface walks users through the entire modeling process while providing comprehensive control over every aspect of the modeling journey, ensuring a seamless experience. Ultimately, alvaModel stands out as a valuable asset for chemists and researchers aiming to enhance their modeling capabilities.

Enhance your operational efficiency with superior data through AssetFuture’s innovative technology, which helps you derive insights and anticipate the lifecycle of your assets. We collaborate with you to illuminate data clarity. Experience enhanced transparency through advanced classification, virtualization, and forecasting of asset lifecycles. Access current data, lifecycle modeling, and interactive dashboards that empower you to manage maintenance expenses, plan for capital investments, and craft future asset strategies. AssetFuture serves as a technological solution that allows organizations to accurately forecast costs, manage risks, and assess the performance of the built environment's lifecycle. Our clients benefit from comprehensive insights into the performance and cost metrics of their asset portfolios, enabling them to make informed decisions quickly and confidently. With real-time visualizations of anticipated capital expenditures across your entire portfolio or for specific assets, AssetFuture provides the adaptability needed to prioritize expenditures, formulate asset renewal initiatives, and implement them efficiently. This results in not only streamlined operations but also a proactive approach to asset management.

Domino is a comprehensive enterprise AI platform that enables organizations to transform AI initiatives into scalable, production-ready systems. It supports the full AI lifecycle, including data access, model development, deployment, and ongoing management. The platform provides a self-service environment where data scientists can access tools, datasets, and compute resources with built-in governance and security controls. Domino allows teams to build machine learning models, generative AI applications, and intelligent agents using their preferred development environments. It also includes advanced orchestration capabilities to manage workloads across hybrid, multi-cloud, and on-premises infrastructures. Governance features such as model registries, audit trails, and policy enforcement ensure compliance and reproducibility. The platform enhances collaboration by providing a centralized system of record for all AI assets and experiments. Additionally, it helps organizations optimize costs through resource management and usage tracking. Domino is designed to meet enterprise standards for security and regulatory compliance. Ultimately, it empowers businesses to accelerate AI innovation while maintaining operational control and accountability.

Deeploy empowers users to maintain oversight of their machine learning models. With our responsible AI platform, you can effortlessly deploy your models while ensuring that transparency, control, and compliance are upheld. In today's landscape, the significance of transparency, explainability, and security in AI models cannot be overstated. By providing a secure environment for model deployment, you can consistently track your model's performance with assurance and responsibility. Throughout our journey, we have recognized the critical role that human involvement plays in the realm of machine learning. When machine learning systems are designed to be explainable and accountable, it enables both experts and consumers to offer valuable feedback, challenge decisions when warranted, and foster a sense of trust. This understanding is precisely why we developed Deeploy, to bridge the gap between advanced technology and human oversight. Ultimately, our mission is to facilitate a harmonious relationship between AI systems and their users, ensuring that ethical considerations are always at the forefront.