Alternatives to Robust Intelligence

It works: Codeless testing Give your entire team the ability to create and maintain automated tests. Not just developers. Meet your testing demands fast. You can get full coverage of your tests in days and not months. Our natural-language tests are very resistant to code changes. Our AI will quickly repair any test failures. Continuous Testing is a key component of Agile/DevOps. Push features to production in the same day. Boozang supports the following test approaches: - Codeless Record/Replay interface - BDD / Cucumber - API testing - Model-based testing - HTML Canvas testing The following features makes your testing a breeze - In-browser console debugging - Screenshots to show where test fails - Integrate to any CI server - Test with unlimited parallel workers to speed up tests - Root-cause analysis reports - Trend reports to track failures and performance over time - Test management integration (Xray / Jira)

Software for continuous performance testing to automate API load and application testing. For complex applications, you can design code-free performance tests. Script performance tests in automated pipelines for API test. You can design, maintain, and run performance tests in code. Then analyze the results within continuous integration pipelines with pre-packaged plugins for CI/CD tools or the NeoLoad API. You can quickly create test scripts for large, complex applications with a graphical user interface. This allows you to skip the tedious task of manually coding new or updated tests. SLAs can be defined based on the built-in monitoring metrics. To determine the app's performance, put pressure on it and compare SLAs with server-level statistics. Automate pass/fail triggers using SLAs. Contributes to root cause analysis. Automatic test script updates make it easier to update test scripts. For easy maintenance, update only the affected part of the test and re-use any remaining.

ReliaSoft provides a powerful set of reliability software solutions that facilitate a comprehensive range of reliability engineering modeling techniques and analysis techniques. We are the leading provider of reliability solutions for product testing, design, maintenance strategies and optimization. Our products support a variety of reliability and maintainability techniques, including life data analysis, accelerated lifetime testing, system modeling and RAM analysis. We also support reliability growth, FRACAS analysis, FMEA analysis and RCM analysis. These tools help you improve the reliability of your products and processes, and optimize maintenance planning.

Zebrunner is an AI-driven platform that seamlessly integrates manual and automated testing, enabling QA teams to collaborate efficiently. Its AI-enhanced capabilities streamline testing workflows by generating and autocompleting test cases, prioritizing failures, and delivering real-time insights. Zebrunner Test Case Management offers a powerful solution for organizing manual test cases. It features intuitive split-screen navigation, drag-and-drop editing, and customizable layouts, making test management more efficient. With real-time progress tracking, testers can quickly identify and resolve blockers or failures. Custom fields and filters enable tailored test management to fit project-specific requirements. Zebrunner Automation Reporting enhances automated testing by delivering advanced reporting and analytics. It integrates with popular frameworks such as TestNG, JUnit, and Cypress, providing real-time insights into test execution. Teams can easily monitor progress, analyze failures, and access logs or video recordings. With its comprehensive reporting capabilities, Zebrunner offers data-driven insights, helping teams optimize their testing efforts and make more informed decisions.

DigitalClone®, for Engineering is the only software that integrates multiple scales of analysis into a single package. It is the world's best gearbox reliability prediction tool. DC-E, in addition to the modeling and analysis capabilities at the level of the gearbox and the gear/bearing, is the only software that models fatigue life using detailed, physics-based models (US Patent 10474772B2). DC-E allows the construction of a digital twin of a gearbox. This includes all stages of the asset's lifecycle, from design and manufacturing optimization to supplier selection to failure root cause analysis to condition based maintenance and prognostics. This computational environment reduces the time and cost of bringing new designs to market and maintaining them over time.

Shield34 stands out as the sole web automation framework that ensures complete compatibility with Selenium, allowing users to seamlessly continue utilizing their existing Selenium scripts while also enabling the creation of new ones through the Selenium API. It effectively tackles the notorious issue of flaky tests by implementing self-healing technology, intelligent defenses, error recovery protocols, and dynamic element locators. Furthermore, it offers AI-driven anomaly detection and root cause analysis, which facilitates a swift examination of failed tests to identify what changed and triggered the failure. By eliminating flaky tests, which often present significant challenges, Shield34 incorporates sophisticated defense-and-recovery AI algorithms into each Selenium command, including dynamic element locators, thereby reducing false positives and promoting self-healing alongside maintenance-free testing. Additionally, with its real-time root cause analysis capabilities powered by AI, Shield34 can swiftly identify the underlying reasons for test failures, minimizing the burden of debugging and the effort required to replicate issues. Ultimately, users can relish a more intelligent version of Selenium, as it effortlessly integrates with your existing testing framework while enhancing overall efficiency.

Mindgard, the leading cybersecurity platform for AI, specialises in securing AI/ML models, encompassing LLMs and GenAI for both in-house and third-party solutions. Rooted in the academic prowess of Lancaster University and launched in 2022, Mindgard has rapidly become a key player in the field by tackling the complex vulnerabilities associated with AI technologies. Our flagship service, Mindgard AI Security Labs, reflects our dedication to innovation, automating AI security testing and threat assessments to identify and remedy adversarial threats that traditional methods might miss due to their complexity. Our platform is supported by the largest, commercially available AI threat library, enabling organizations to proactively protect their AI assets across their entire lifecycle. Mindgard seamlessly integrates with existing security ecosystem platforms, enabling Security Operations Centers (SOCs) to rapidly onboard AI/ML solutions and manage AI-specific vulnerabilities and hence risk.

Arize's machine-learning observability platform automatically detects and diagnoses problems and improves models. Machine learning systems are essential for businesses and customers, but often fail to perform in real life. Arize is an end to-end platform for observing and solving issues in your AI models. Seamlessly enable observation for any model, on any platform, in any environment. SDKs that are lightweight for sending production, validation, or training data. You can link real-time ground truth with predictions, or delay. You can gain confidence in your models' performance once they are deployed. Identify and prevent any performance or prediction drift issues, as well as quality issues, before they become serious. Even the most complex models can be reduced in time to resolution (MTTR). Flexible, easy-to use tools for root cause analysis are available.

Effortlessly design, execute, and evaluate tests with a focus on enhancing collaboration among development, testing, product, and DevOps teams, enabling them to deliver high-quality products in record time. Leverage your tests in a production environment while tracking business transactions efficiently. CloudBeat is designed for DevOps and developers alike, offering cross-region, device, and browser compatibility. It also enables comprehensive monitoring of user experience and service level agreements (SLAs), providing an in-depth performance analysis. With features such as intelligent root-cause analysis, real-time alerts, and daily updates, it supports both SaaS and on-premise deployment. This centralized continuous quality platform streamlines the creation, execution, and assessment of unit, API, integration, and end-to-end tests within a DevOps setting. Furthermore, CloudBeat integrates effortlessly with leading testing frameworks and CI tools, allowing for the execution of extensive test suites through built-in parallelization, test lab oversight, and failure analysis. Our goal is to elevate your software quality, minimize testing and development durations, and ultimately enhance customer satisfaction. By utilizing CloudBeat, teams can ensure a more efficient workflow and achieve better results.

Transforming materials data into superior products at an accelerated pace enhances research and development, streamlines scaling processes, and optimizes quality control and supply chain decisions. This approach enables the discovery of innovative materials while utilizing machine learning guidance to predict outcomes, leading to swifter and more effective results. As you progress towards production, you can construct a model that tests the boundaries of your products, facilitating the design of cost-effective and resilient production lines. Furthermore, these models can forecast potential failures by analyzing the supplied materials informatics alongside production line parameters. The Materials Zone platform compiles data from various independent sources, including materials suppliers and manufacturing facilities, ensuring secure communication between them. By leveraging machine learning algorithms on your experimental data, you can identify new materials with tailored properties, create ‘recipes’ for their synthesis, develop tools for automatic analysis of unique measurements, and gain valuable insights. This holistic approach not only enhances the efficiency of R&D but also fosters collaboration across the materials ecosystem, ultimately driving innovation forward.

Examine the causes of failures right after the testing phase concludes. Create straightforward and easy-to-read reports for your teams. Utilize machine learning-driven auto-analyzers to delve into the reasons behind the failures. Consolidate test outcomes from different platforms, frameworks, and programming languages while delivering actionable insights. Employing machine learning algorithms helps to uncover patterns in the test data, identify the underlying causes of failures, and forecast future testing outcomes. Support the manual examination of test logs and emerging failure patterns from the latest test runs. Enable automated decision-making processes for release pipelines by adhering to defined testing criteria and outcomes. Present test results in a clear format that facilitates monitoring of trends, recognition of patterns, generation of insights, and informed business choices. Regularly assess your product's health and automate release decisions with Quality Gates to enhance efficiency and reliability. This approach not only streamlines the testing process but also significantly contributes to improving overall product quality.

Easily train and deploy large-scale AI models with just a single command by pointing to your S3 bucket—then let us take care of everything else, including orchestration, efficiency, node failures, and infrastructure management. The process is straightforward and scalable, allowing you to utilize MosaicML to train and serve large AI models using your own data within your secure environment. Stay ahead of the curve with our up-to-date recipes, techniques, and foundation models, all developed and thoroughly tested by our dedicated research team. With only a few simple steps, you can deploy your models within your private cloud, ensuring that your data and models remain behind your own firewalls. You can initiate your project in one cloud provider and seamlessly transition to another without any disruptions. Gain ownership of the model trained on your data while being able to introspect and clarify the decisions made by the model. Customize content and data filtering to align with your business requirements, and enjoy effortless integration with your existing data pipelines, experiment trackers, and other essential tools. Our solution is designed to be fully interoperable, cloud-agnostic, and validated for enterprise use, ensuring reliability and flexibility for your organization. Additionally, the ease of use and the power of our platform allow teams to focus more on innovation rather than infrastructure management.

Obtain scores that assess factual accuracy, context retrieval quality, guideline compliance, tonality, among other metrics. Improvement is impossible without measurement. UpTrain consistently evaluates your application's performance against various criteria and notifies you of any declines, complete with automatic root cause analysis. This platform facilitates swift and effective experimentation across numerous prompts, model providers, and personalized configurations by generating quantitative scores that allow for straightforward comparisons and the best prompt selection. Hallucinations have been a persistent issue for LLMs since their early days. By measuring the extent of hallucinations and the quality of the retrieved context, UpTrain aids in identifying responses that lack factual correctness, ensuring they are filtered out before reaching end-users. Additionally, this proactive approach enhances the reliability of responses, fostering greater trust in automated systems.

ATA is an intelligent API management platform driven by AI that consolidates design, testing, governance, documentation, and lifecycle workflows into a cohesive workspace, enabling teams to efficiently design, develop, test, maintain, and oversee APIs with enhanced quality and collaboration. This platform ensures that API code, design documentation, specifications, test cases, and release notes are consistently synchronized, minimizing manual tasks and discrepancies while accommodating OpenAPI specifications, facilitating frontend progress with mock servers even before the backend is ready, and enabling scheduled API monitoring to catch slow responses, timeouts, or failures at an early stage. Additionally, it features a Developer Studio that supports design-first OpenAPI creation and version control, offers end-to-end test automation with AI-generated robustness and security assessments, includes mock servers, and enables chained API workflows along with UI automation testing, all backed by a Documentation Portal that centralizes API documentation with support for multiple editors, version management, secure access controls, and automatically linked live endpoints. Ultimately, ATA streamlines the entire API lifecycle, fostering better collaboration and efficiency among development teams.

Giskard provides interfaces to AI & Business teams for evaluating and testing ML models using automated tests and collaborative feedback. Giskard accelerates teamwork to validate ML model validation and gives you peace-of-mind to eliminate biases, drift, or regression before deploying ML models into production.

Receive precise notifications of potential failures weeks or even months ahead by utilizing real-time information and predictive analytics. Make use of an integrated approach that includes prescriptive maintenance, root cause analysis, and RAM analysis to tackle problems at various levels, including equipment, process, and system. Efficiently implement automated Asset Performance Management solutions using minimal intervention machine learning techniques to foresee asset failures and minimize downtime across the entire plant, across systems, or in multiple sites. This proactive strategy not only enhances operational efficiency but also significantly boosts overall productivity.

Robust simulation software designed to enhance asset performance is available, offering features such as maintenance and spare parts optimization, availability assessments, reliability-centered maintenance strategies, life cycle cost analyses, and accelerated life testing, all within a single cohesive platform. This software seamlessly integrates with your SAP or MAXIMO systems for comprehensive data analysis. It allows for the identification of critical machinery and the automatic generation of failure models through Weibull analysis. By leveraging simulation, you can refine your maintenance strategies and cut expenses. Additionally, the tool predicts system availability while optimizing design processes. It also facilitates the simulation of capacity for multiple products, incorporating target cost penalties. You can model interdependencies within systems using reliability block diagrams (RBDs) or fault trees. Operational rules can be embedded to ensure accurate performance simulations. Furthermore, it helps in determining the optimal spare parts inventory strategy. Life cycle costs can be predicted, and the ALT module allows for the analysis of test data related to stressed failures. Lastly, the software enables the identification of performance trends within the process reliability module, providing valuable insights for continuous improvement.

MLBox is an advanced Python library designed for Automated Machine Learning. This library offers a variety of features, including rapid data reading, efficient distributed preprocessing, comprehensive data cleaning, robust feature selection, and effective leak detection. It excels in hyper-parameter optimization within high-dimensional spaces and includes cutting-edge predictive models for both classification and regression tasks, such as Deep Learning, Stacking, and LightGBM, along with model interpretation for predictions. The core MLBox package is divided into three sub-packages: preprocessing, optimization, and prediction. Each sub-package serves a specific purpose: the preprocessing module focuses on data reading and preparation, the optimization module tests and fine-tunes various learners, and the prediction module handles target predictions on test datasets, ensuring a streamlined workflow for machine learning practitioners. Overall, MLBox simplifies the machine learning process, making it accessible and efficient for users.

An open-source platform for monitoring machine learning models offers robust observability features. It allows users to evaluate, test, and oversee models throughout their journey from validation to deployment. Catering to a range of data types, from tabular formats to natural language processing and large language models, it is designed with both data scientists and ML engineers in mind. This tool provides everything necessary for the reliable operation of ML systems in a production environment. You can begin with straightforward ad hoc checks and progressively expand to a comprehensive monitoring solution. All functionalities are integrated into a single platform, featuring a uniform API and consistent metrics. The design prioritizes usability, aesthetics, and the ability to share insights easily. Users gain an in-depth perspective on data quality and model performance, facilitating exploration and troubleshooting. Setting up takes just a minute, allowing for immediate testing prior to deployment, validation in live environments, and checks during each model update. The platform also eliminates the hassle of manual configuration by automatically generating test scenarios based on a reference dataset. It enables users to keep an eye on every facet of their data, models, and testing outcomes. By proactively identifying and addressing issues with production models, it ensures sustained optimal performance and fosters ongoing enhancements. Additionally, the tool's versatility makes it suitable for teams of any size, enabling collaborative efforts in maintaining high-quality ML systems.

Integrate your datasets and models into Openlayer while collaborating closely with the entire team to establish clear expectations regarding quality and performance metrics. Thoroughly examine the reasons behind unmet objectives to address them effectively and swiftly. You have access to the necessary information for diagnosing the underlying causes of any issues. Produce additional data that mirrors the characteristics of the targeted subpopulation and proceed with retraining the model accordingly. Evaluate new code commits against your outlined goals to guarantee consistent advancement without any regressions. Conduct side-by-side comparisons of different versions to make well-informed choices and confidently release updates. By quickly pinpointing what influences model performance, you can save valuable engineering time. Identify the clearest avenues for enhancing your model's capabilities and understand precisely which data is essential for elevating performance, ensuring you focus on developing high-quality, representative datasets that drive success. With a commitment to continual improvement, your team can adapt and iterate efficiently in response to evolving project needs.

Craft personalized monitoring solutions for your machine learning models using our incredibly intuitive monitor builder, which alerts you to problems such as concept drift, declines in model performance, and bias, among other issues. Aporia effortlessly integrates with any machine learning infrastructure, whether you're utilizing a FastAPI server on Kubernetes, an open-source deployment solution like MLFlow, or a comprehensive machine learning platform such as AWS Sagemaker. Dive into specific data segments to meticulously observe your model's behavior. Detect unforeseen bias, suboptimal performance, drifting features, and issues related to data integrity. When challenges arise with your ML models in a production environment, having the right tools at your disposal is essential for swiftly identifying the root cause. Additionally, expand your capabilities beyond standard model monitoring with our investigation toolbox, which allows for an in-depth analysis of model performance, specific data segments, statistics, and distributions, ensuring you maintain optimal model functionality and integrity.

Sensai offers a cutting-edge AI-driven platform for detecting anomalies, performing root cause analysis, and forecasting issues, which allows for immediate problem resolution. The Sensai AI solution greatly enhances system uptime and accelerates the identification of root causes. By equipping IT leaders with the tools to effectively manage service level agreements (SLAs), it boosts both performance and profitability. Additionally, it automates and simplifies the processes of anomaly detection, prediction, root cause analysis, and resolution. With its comprehensive perspective and integrated analytics, Sensai seamlessly connects with third-party tools. Users benefit from pre-trained algorithms and models available from the outset, ensuring a swift and efficient implementation. This holistic approach helps organizations maintain operational efficiency while proactively addressing potential disruptions.

We allow humans to excel in their strengths while machines operate at their best capabilities. The walrus.ai command-line interface is the simplest method to conduct comprehensive tests for your application. It enables you to define tests directly within the command or utilize designated YML files for organization. You can submit your tests either through our dashboard or using the walrus.ai CLI. Your provided instructions are converted into an automated testing framework by our system. Test results can be accessed via our dashboard, through the command line, or through various integrations. We keep a close watch on each model execution to detect any changes in the application or potential false failures. By re-verifying your tests, we guarantee that you will not encounter misleading results, either positive or negative. You can assess even the most complex user interactions using simple language — we take care of everything else, ensuring a seamless experience. This allows you to focus on developing your application while we manage the intricacies of testing.

Deductive AI is an innovative platform that transforms the way organizations address intricate system failures. By seamlessly integrating your entire codebase with telemetry data, which includes metrics, events, logs, and traces, it enables teams to identify the root causes of problems with remarkable speed and accuracy. This platform simplifies the debugging process, significantly minimizing downtime and enhancing overall system dependability. With its ability to integrate with your codebase and existing observability tools, Deductive AI constructs a comprehensive knowledge graph that is driven by a code-aware reasoning engine, effectively diagnosing root issues similar to a seasoned engineer. It rapidly generates a knowledge graph containing millions of nodes, revealing intricate connections between the codebase and telemetry data. Furthermore, it orchestrates numerous specialized AI agents to meticulously search for, uncover, and analyze the subtle indicators of root causes dispersed across all linked sources, ensuring a thorough investigative process. This level of automation not only accelerates troubleshooting but also empowers teams to maintain higher system performance and reliability.

InsightFinder Unified Intelligence Engine platform (UIE) provides human-centered AI solutions to identify root causes of incidents and prevent them from happening. InsightFinder uses patented self-tuning, unsupervised machine learning to continuously learn from logs, traces and triage threads of DevOps Engineers and SREs to identify root causes and predict future incidents. Companies of all sizes have adopted the platform and found that they can predict business-impacting incidents hours ahead of time with clearly identified root causes. You can get a complete overview of your IT Ops environment, including trends and patterns as well as team activities. You can also view calculations that show overall downtime savings, cost-of-labor savings, and the number of incidents solved.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.

Effortlessly deploy all leading machine learning frameworks like Pytorch, Onnx, and XGBoost with a single line of code. If you lack your own models, take advantage of our prebuilt options that are optimized for performance with sub-second latency. You can also fine-tune smaller models for specific tasks, which helps to reduce both costs and latency while enhancing overall performance. With just a few lines of code, you can avoid the hassle of managing infrastructure because we handle that for you. Seamlessly integrate with premier ML observability platforms to receive alerts about any feature or prediction drift, allowing for quick comparisons between model versions and prompt issue resolution. Additionally, you can identify the root causes of prediction and feature drift to tackle any decline in model performance effectively. Gain insights into which features are most influential in driving your model's performance, empowering you to make informed adjustments. This comprehensive approach ensures that your machine learning processes are both efficient and effective.

Tango™, Reliability Information Management standardizes and integrates facility reliability information. This creates accountability and ensures that the proper procedures are followed to prevent or eliminate failures. Tango™, offers a variety services to tailor a solution for your facility. Equipment Management, Condition Management and RoundsLogging are all available to provide the best coverage for managing your Reliability Information. Tango™, which offers many features, includes: Lifecycle Tracking and Condition Management Programs. Integrated Condition Reports. Task Management. Equipment Management Programs. Physical Inspections. Vibration Analysis. Root Cause Failure Analysis. Repair History. Asset & Warranty Information. Oil Analysis. Oil Sample Management. Test Result Sharing. Repair/Test Status.

Ansys PathFinder-SC serves as a robust and scalable solution designed to facilitate the planning, verification, and approval of IP and full-chip SoC designs, ensuring their integrity and resilience against electrostatic discharge (ESD). This innovative tool effectively identifies and isolates the underlying sources of design problems that could lead to chip failures due to charged-device model (CDM), human body model (HBM), or various ESD incidents. With its cloud-native architecture capable of harnessing thousands of compute cores, PathFinder-SC significantly accelerates full-chip turnaround times. Endorsed by leading foundries for current density assessments and ESD approval, it stands out as a reliable choice in the industry. The platform's comprehensive data modeling, extraction, and transient simulation engine provides an all-encompassing solution for ESD verification. Utilizing a single-pass model, it seamlessly reads industry-standard design formats, establishes ESD rules, extracts RCs for the power network, and conducts ESD simulations to pinpoint root causes while offering repair and optimization suggestions, all consolidated within one powerful tool. This streamlined process not only enhances efficiency but also reduces the time-to-market for critical design projects.

Boofuzz represents a continuation and enhancement of the established Sulley fuzzing framework. In addition to a variety of bug fixes, Boofuzz emphasizes extensibility and flexibility. Mirroring Sulley, it integrates essential features of a fuzzer, such as rapid data generation, instrumentation, failure detection, and the ability to reset targets after a failure, along with the capability to log test data effectively. It offers a more streamlined installation process and accommodates diverse communication mediums. Furthermore, it includes built-in capabilities for serial fuzzing, as well as support for Ethernet, IP-layer, and UDP broadcasting. The improvements in data recording are notable, providing consistency, clarity, and thoroughness in the results. Users benefit from the ability to export test results in CSV format and enjoy extensible instrumentation and failure detection options. Boofuzz operates as a Python library that facilitates the creation of fuzzer scripts, and setting it up within a virtual environment is highly advisable for optimal performance and organization. This attention to detail and user experience makes Boofuzz a powerful tool for security testing.

Ansys LS-DYNA stands out as the leading explicit simulation software widely utilized for various applications, including drop testing, impact analysis, penetration scenarios, collisions, and ensuring occupant safety. Renowned as the most extensively used explicit simulation tool globally, Ansys LS-DYNA excels in modeling the behavior of materials subjected to brief yet intense loading conditions. Its comprehensive suite of elements, contact formulations, and material models enables the simulation of intricate models while allowing precise control over every aspect of the issue at hand. The software offers a broad range of analyses, boasting rapid and effective parallel processing capabilities. Engineers can investigate simulations that involve material failure, examining how such failures evolve through components or entire systems. Additionally, LS-DYNA adeptly manages models with numerous interacting parts or surfaces, ensuring that the interactions and load transfers between complex behaviors are accurately represented. This capability makes LS-DYNA an invaluable tool for engineers facing multifaceted simulation challenges.

Scale Data Engine empowers machine learning teams to enhance their datasets effectively. By consolidating your data, authenticating it with ground truth, and incorporating model predictions, you can seamlessly address model shortcomings and data quality challenges. Optimize your labeling budget by detecting class imbalances, errors, and edge cases within your dataset using the Scale Data Engine. This platform can lead to substantial improvements in model performance by identifying and resolving failures. Utilize active learning and edge case mining to discover and label high-value data efficiently. By collaborating with machine learning engineers, labelers, and data operations on a single platform, you can curate the most effective datasets. Moreover, the platform allows for easy visualization and exploration of your data, enabling quick identification of edge cases that require labeling. You can monitor your models' performance closely and ensure that you consistently deploy the best version. The rich overlays in our powerful interface provide a comprehensive view of your data, metadata, and aggregate statistics, allowing for insightful analysis. Additionally, Scale Data Engine facilitates visualization of various formats, including images, videos, and lidar scenes, all enhanced with relevant labels, predictions, and metadata for a thorough understanding of your datasets. This makes it an indispensable tool for any data-driven project.

BrowsingBee is an innovative browser testing platform powered by AI that revolutionizes the way automated testing is performed by allowing users to create tests using simple English phrases while providing resilience against UI modifications through adaptive, self-healing scripts. With its user-friendly interface, it crafts durable tests that automatically adapt to changes in elements, captures each test run through video recordings for detailed debugging, and utilizes AI-driven insights to identify potential bugs before they are deployed in production environments. The platform caters to diverse testing requirements, including regression tests and user journey workflows, such as the processes from signup to purchase, in addition to ensuring cross-browser compatibility across major browsers like Chrome, Firefox, Safari, and Edge. It also evaluates performance metrics like page load speeds and API response times, making it a comprehensive solution for developers. Users have the option to schedule tests for continuous application monitoring and receive intelligent notifications through Slack, email, or webhooks, keeping teams promptly informed of any failures that may arise. This level of automation not only enhances efficiency but also significantly reduces the time needed to troubleshoot issues, ultimately leading to a more reliable user experience.

Perfecto is the leading testing platform for web and mobile apps. Our belief is that your apps should work regardless of the circumstances. Perfecto's cloud-based software allows you to increase test coverage and reduce the number of escaped defects, while speeding up testing. Perfecto offers a unified solution that covers all aspects of mobile and web testing, from creation to execution and analysis. You can test in your CI rather than at the end of the cycle and quickly identify failures with false-negative filtering. You can align scenario and platform coverage with actual users. Test failure analysis provides real test failure reasons. You can get quick feedback with heatmaps, test reports, or CI dashboards. You will get the most complete rich test artifacts available, including screenshots, crash logs, and HAR files. Visual validation allows for side-by-side comparisons across platforms. Reduce bug reproduction time. Fix bugs in your IDE. Integrate Jira fully for complete test management.

QASolve.ai is a no-code platform powered by artificial intelligence, aimed at achieving rapid application quality assurance while minimizing the need for manual involvement. With the promise of generating over 80% of test automation within a single week, it leverages an AI model capable of crafting tests without the necessity for source code, specifications, or human scripting. The platform incorporates self-healing technology to minimize flaky tests and facilitates extensive parallel execution across various platforms and devices, enabling teams to conduct thorough test suites efficiently. Upon registering their application URL and roles, users benefit from QASolve’s “Discovery” AI agents, which meticulously analyze user journeys, workflows, and relationships to generate relevant test cases and test data. These agents also seamlessly integrate into CI/CD pipelines through APIs, offering dashboards that deliver real-time insights, failure analysis, and ongoing maintenance of tests through different software releases. Additionally, QASolve.ai allows for the export of tests to popular frameworks such as Playwright or Selenium, thereby preventing vendor lock-in and providing flexibility for development teams. This comprehensive approach not only streamlines the testing process but also enhances overall application quality assurance.

Having the most skilled developers isn't enough if testing processes are hindering their progress; in fact, a staggering 80% of your software tests may be ineffective. The challenge lies in identifying which 80% is truly unnecessary. We utilize your data to pinpoint the essential 20%, enabling you to accelerate your release process. Our predictive test selection tool, inspired by machine learning techniques employed by leading companies like Facebook, is designed for easy adoption by any organization. We accommodate a variety of programming languages, test frameworks, and continuous integration systems—just integrate Git into your workflow. Launchable employs machine learning to evaluate your test failures alongside your source code, sidestepping traditional code syntax analysis. This flexibility allows Launchable to effortlessly extend its support to nearly any file-based programming language, ensuring it can adapt to various teams and projects with differing languages and tools. Currently, we provide out-of-the-box support for languages including Python, Ruby, Java, JavaScript, Go, C, and C++, with a commitment to continually expand our offerings as new languages emerge. In this way, we help organizations streamline their testing process and enhance overall efficiency.

The Measuring and Assessing Individual Potential test is a personality assessment grounded in the Five Factor Model, recognized as the most reliable and well-documented framework for evaluating personality traits. This tool provides an impartial and objective evaluation of personality traits useful in the recruitment process. Equipped with suitability scores, it enables a data-informed method for selecting leaders. The assessment is not only valuable for screening candidates but also for predicting work behaviors at both managerial and peer levels. Its applications extend to onboarding evaluations, leadership enhancement, and career guidance. Are you in the process of hiring, or are you eager to discover how our distinctive testing instruments can benefit your organization in various capacities? The MAP assessment reflects an individual's typical behaviors, while the MAP-X tool can further assess extreme personality traits, including risk behaviors that manifest under conditions of stress, pressure, and fatigue, thus providing a comprehensive understanding of an individual’s potential in a work environment. This dual approach ensures that organizations can make well-informed decisions based on a thorough understanding of personality dynamics.

With LINK Services, you have immediate access to test specifications, supplier communications, test failures, lab notifications, corrective actions, and performance metrics. This innovative application for managing Product Compliance Testing is designed specifically to streamline the processes involved in product and material testing and conformity. It offers supply chain partners a suite of tools to effectively organize and communicate about tasks and analytics related to testing programs, including test ordering, results, lab reporting, corrective actions, product certificates, bills of materials, and other crucial quality management activities. By connecting brand and retailer enterprise systems such as PLM and ERP/SAP with suppliers and testing laboratories—whether in-house or third-party—LINK simplifies and consolidates all relevant data into one cohesive, lab-agnostic platform. This ensures that all stakeholders can efficiently access and share critical information, ultimately enhancing collaboration and compliance across the entire supply chain.

Traversal is an innovative AI-driven Site Reliability Engineering (SRE) solution that functions round the clock, autonomously identifying, addressing, and even preventing production issues. It meticulously analyzes logs, metrics, traces, and your codebase to pinpoint the root causes of errors or delays, quickly highlighting the impacted areas, critical bottleneck services, and potential root causes with relevant evidence in a matter of minutes. Leveraging advancements in causal machine learning, reasoning from large language models, and intelligent AI agents, Traversal proactively resolves problems before alerts are triggered, ensuring seamless operations. Tailored for complex organizations and vital infrastructure, it accommodates diverse data types, supports bring-your-own models, and offers optional on-premises deployment for added flexibility. With its straightforward integration into existing systems requiring only read-only access—without the need for agents, sidecars, or any write operations to production—Traversal guarantees data privacy and control. By effortlessly fitting into your observability framework, it not only accelerates the resolution process but also significantly reduces downtime, further enhancing operational efficiency and reliability. Furthermore, its ability to adapt to various environments makes it a versatile asset for businesses striving for uninterrupted service delivery.

Discover a revolutionary approach to training machine learning models directly on your Mac with Create ML, which simplifies the process while delivering robust Core ML models. You can train several models with various datasets all within one cohesive project. Utilize Continuity to preview your model's performance by connecting your iPhone's camera and microphone to your Mac, or simply input sample data for evaluation. The training process allows you to pause, save, resume, and even extend as needed. Gain insights into how your model performs against test data from your evaluation set and delve into essential metrics, exploring their relationships to specific examples, which can highlight difficult use cases, guide further data collection efforts, and uncover opportunities to enhance model quality. Additionally, if you want to elevate your training performance, you can integrate an external graphics processing unit with your Mac. Experience the lightning-fast training capabilities available on your Mac that leverage both CPU and GPU resources, and take your pick from a diverse selection of model types offered by Create ML. This tool not only streamlines the training process but also empowers users to maximize the effectiveness of their machine learning endeavors.

Gauge is an open-source framework that allows you to write and run acceptance tests. Gauge tests can be written in Markdown, which makes it easier to maintain and write them. Reuse specifications and robust, refactoring will reduce duplication. A test suite that is less code and has readable specifications will save you time and effort. Gauge can be used with multiple languages, CI/CD tools, and automation drivers. To get your test automation tool working for you, you don't need to learn a new language. Gauge's plugin architecture and ecosystem is robust. Gauge can be easily extended to support IDEs, drivers and datasources. Do not waste your time looking through stacktraces. Gauge will take a screenshot of a test failure to give you a clear picture of what went wrong. Reports can be accessed in multiple formats (XML and JSON, HTML).

Our groundbreaking tool transforms the landscape of API development and testing, ensuring that the process is straightforward, visual, and open to all users. Wave farewell to intricate commands and tedious coding—effortlessly create mock REST API endpoints using our user-friendly graphical interface. What Makes Our Mock API Tool Stand Out? No Coding Necessity: Build APIs through visual means, catering to individuals with varying levels of expertise. Robust Automated Testing: Identify potential failure points in the applications that utilize your APIs with our integrated automated testing features. In-Depth Simulations: Evaluate and mimic a wide range of real-world situations to guarantee reliable API functionality. Designed for developers, testers, and newcomers alike, our tool streamlines the entire process, conserving both time and resources while enhancing the overall quality and reliability of your API-based integrations. Users can confidently navigate API challenges with ease, making their development journey far more efficient and enjoyable.

Atla serves as a comprehensive observability and evaluation platform tailored for AI agents, focusing on diagnosing and resolving failures effectively. It enables real-time insights into every decision, tool utilization, and interaction, allowing users to track each agent's execution, comprehend errors at each step, and pinpoint the underlying causes of failures. By intelligently identifying recurring issues across a vast array of traces, Atla eliminates the need for tedious manual log reviews and offers concrete, actionable recommendations for enhancements based on observed error trends. Users can concurrently test different models and prompts to assess their performance, apply suggested improvements, and evaluate the impact of modifications on success rates. Each individual trace is distilled into clear, concise narratives for detailed examination, while aggregated data reveals overarching patterns that highlight systemic challenges rather than mere isolated incidents. Additionally, Atla is designed for seamless integration with existing tools such as OpenAI, LangChain, Autogen AI, Pydantic AI, and several others, ensuring a smooth user experience. This platform not only enhances the efficiency of AI agents but also empowers users with the insights needed to drive continuous improvement and innovation.

IQM Studio is our technology that powers our Integrated Quality Management process. It's made up of our IQM Modeling, and IQM Scripting features. IQM Modeling's powerful test generator algorithm can automate test design in our Cause Effect Models. This takes the guesswork out testing. You can save time and ensure that you are validating system behavior by writing test cases using a model of your system. IQM Scripting can be used in a standalone mode to create automation scripts using keywords. You can also combine both features with IQM Studio to generate automation scripts from your model. IQM Scripting allows everyone in a project to create automation scripts. This includes non-programmer resources such as BA's and SME's.

OPUS is a leading platform for industrial no-code AI that allows users to model equipment and processes. With OPUS you can benefit from: - Process optimization insights - Predictive maintenance - Lower power consumption - Increased productivity - Accurate forecasting - Increased asset reliability - Reduced maintenance costs - Improved planning - ESG reporting and carbon reduction insights Without programming or coding experience, existing teams can get insights from their data and predict future outcomes. Explore your asset's data deeper than ever before. Uncovering unexpected correlations. Root cause analysis can be done on individual components to help you focus your efforts. Automated AI predictive insights can help you plan interventions and make informed business decisions. With rapid deployment and AI model results within minutes of being built, you can unleash the power of your existing operational data and achieve real ROI, using your existing team of asset engineers, operators and maintenance managers. Optimize your entire facility, plant, or work site, discover the power of automated AI.