Alternatives to RagaAI

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection.

You know that you could be testing more to catch bugs earlier, but QA testing can take a lot of time, effort and resources to do it right. MuukTest can get growing engineering teams up to 95% coverage of end-to-end tests in just 3 months. Our QA experts create, manage, maintain, and update E2E tests on the MuukTest Platform for your web, API, and mobile apps at record speed. We begin exploratory and negative tests after achieving 100% regression coverage within 8 weeks to uncover bugs and increase coverage. The time you spend on development is reduced by managing your testing frameworks, scripts, libraries and maintenance. We also proactively identify flaky tests and false test results to ensure the accuracy of your tests. Early and frequent testing allows you to detect errors in the early stages your development lifecycle. This reduces the burden of technical debt later on.

Testsigma is a low-code end-to-end test automation platform for Agile teams. It lets SDETs, manual testers, SMEs, and QAs collaboratively plan, develop, execute, analyze, debug, and report on their automated testing for websites, native Android and iOS apps, and APIs. It is available as a fully managed, cloud-based solution as well as a self-hosted instance that is open source (Testsigma Community Edition). The platform is built with Java, but the automated tests are code-agnostic. Through built-in NLP Grammar, teams can automate user actions in simple English, or generate airtight test scripts with the Test Recorder. With features like built-in visual testing, parametrized or data-driven testing, 2FA testing, and an AI that automatically fixes unstable elements and test steps, identifies and isolates regression-affected scripts, and provides suggestions to help you find and fix test failures, Testsigma can replace tens of different tools in the QA toolchain to enable teams to test easily, continuously, and collaboratively.

Parasoft's mission is to provide automated testing solutions and expertise that empower organizations to expedite delivery of safe and reliable software. A powerful unified C and C++ test automation solution for static analysis, unit testing and structural code coverage, Parasoft C/C++test helps satisfy compliance with industry functional safety and security requirements for embedded software systems.

DagsHub, a collaborative platform for data scientists and machine-learning engineers, is designed to streamline and manage their projects. It integrates code and data, experiments and models in a unified environment to facilitate efficient project management and collaboration. The user-friendly interface includes features such as dataset management, experiment tracker, model registry, data and model lineage and model registry. DagsHub integrates seamlessly with popular MLOps software, allowing users the ability to leverage their existing workflows. DagsHub improves machine learning development efficiency, transparency, and reproducibility by providing a central hub for all project elements. DagsHub, a platform for AI/ML developers, allows you to manage and collaborate with your data, models and experiments alongside your code. DagsHub is designed to handle unstructured data, such as text, images, audio files, medical imaging and binary files.

LambdaTest, a cloud-based cross browser test platform, enables enterprises to run web automation tests at scale (through parallel coding). **Selenium Automation Grid & Cypress CLI on LambdaTest** Tests can be run across more than 2,000 browsers, devices, operating systems to improve browser coverage. LambdaTest is a cloud-based Selenium Grid which helps you run Selenium tests faster. It's secure, scalable and reliable. The Cypress CLI, available on LambdaTest allows you to expand Cypress test coverage up to 40+ browser versions across Windows or macOS platforms. Automation testing is not the only option. You can also do manual tests, visual interface tests, and real time tests. **LT Browser – Responsive Web Testing** LambdaTest's LT browser is a groundbreaking developer-oriented tool that helps you assess the responsiveness and usability of your website. Mobile testing is easier with responsive tests that can be run against 50+ resolutions. You can also create unlimited custom devices.

Your platform for developing generative AI and custom copilots. Use pre-built and customizable AI model on your data to build solutions faster. Explore a growing collection of models, both open-source and frontier-built, that are pre-built and customizable. Create AI models using a code first experience and an accessible UI validated for accessibility by developers with disabilities. Integrate all your OneLake data into Microsoft Fabric. Integrate with GitHub codespaces, Semantic Kernel and LangChain. Build apps quickly with prebuilt capabilities. Reduce wait times by personalizing content and interactions. Reduce the risk for your organization and help them discover new things. Reduce the risk of human error by using data and tools. Automate operations so that employees can focus on more important tasks.

Use tools to bring LLM-powered features into production, including tools for rapid engineering, semantic searching, version control, quantitative testing, and performance monitoring. Compatible with all major LLM providers. Develop an MVP quickly by experimenting with various prompts, parameters and even LLM providers. Vellum is a low-latency and highly reliable proxy for LLM providers. This allows you to make version controlled changes to your prompts without needing to change any code. Vellum collects inputs, outputs and user feedback. These data are used to build valuable testing datasets which can be used to verify future changes before going live. Include dynamically company-specific context to your prompts, without managing your own semantic searching infrastructure.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

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!

Promptfoo identifies and eliminates LLM risks prior to their being shipped into production. Its founders are experienced in launching and scaling AI for over 100M users, using automated red-teaming, testing, and compliance to overcome security, regulatory, and compliance issues. Promptfoo is the most widely used tool in this area, with more than 20,000 users, thanks to its open source, developer first approach. Custom probes that are tailored to your application and identify the failures you care about. Not just generic jailbreaks or prompt injections. With a command-line, live reloads and caching, you can move quickly. No SDKs or cloud dependencies. Open-source software used by teams that serve millions of users, and supported by a vibrant community. Build RAGs, models and prompts that are reliable, based on benchmarks that are specific to your use-case. Automated red teaming and pentesting will help you secure your apps. Accelerate evaluations by using caching, concurrency and live reloading.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

With a suite observability tools, you can confidently evaluate, test and ship LLM apps across your development and production lifecycle. Log traces and spans. Define and compute evaluation metrics. Score LLM outputs. Compare performance between app versions. Record, sort, find, and understand every step that your LLM app makes to generate a result. You can manually annotate and compare LLM results in a table. Log traces in development and production. Run experiments using different prompts, and evaluate them against a test collection. You can choose and run preconfigured evaluation metrics, or create your own using our SDK library. Consult the built-in LLM judges to help you with complex issues such as hallucination detection, factuality and moderation. Opik LLM unit tests built on PyTest provide reliable performance baselines. Build comprehensive test suites for every deployment to evaluate your entire LLM pipe-line.

Release high-quality LLM applications quickly without compromising testing. Never let the subjective and complex nature of LLM interactions hold you back. Generative AI produces subjective results. A subject matter expert must manually check a generated text to determine its quality. You probably know if you're developing an LLM application that you cannot release it without addressing numerous constraints and edge cases. Hallucinations and other issues, such as incorrect answers, bias and deviations from policy, harmful material, and others, need to be identified, investigated, and mitigated both before and after the app is released. Deepchecks allows you to automate your evaluation process. You will receive "estimated annotations", which you can only override if necessary. Our LLM product has been extensively tested and is robust. It is used by more than 1000 companies and integrated into over 300 open source projects. Validate machine-learning models and data in the research and production phases with minimal effort.

BenchLLM allows you to evaluate your code in real-time. Create test suites and quality reports for your models. Choose from automated, interactive, or custom evaluation strategies. We are a group of engineers who enjoy building AI products. We don't want a compromise between the power, flexibility and predictability of AI. We have created the open and flexible LLM tool that we always wanted. CLI commands are simple and elegant. Use the CLI to test your CI/CD pipeline. Monitor model performance and detect regressions during production. Test your code in real-time. BenchLLM supports OpenAI (Langchain), and any other APIs out of the box. Visualize insightful reports and use multiple evaluation strategies.

Symflower improves software development through the integration of static, dynamic and symbolic analyses, as well as Large Language Models. This combination takes advantage of the precision of deterministic analysis and the creativity of LLMs to produce higher quality and faster software. Symflower helps identify the best LLM for a specific project by evaluating models against real-world scenarios. This ensures alignment with specific environments and workflows. The platform solves common LLM problems by implementing automatic post- and pre-processing. This improves code quality, functionality, and efficiency. Symflower improves LLM performance by providing the right context via Retrieval - Augmented Generation (RAG). Continuous benchmarking ensures use cases are effective and compatible with latest models. Symflower also offers detailed reports that accelerate fine-tuning, training, and data curation.

DeepEval is an open-source, easy-to-use framework for evaluating large-language-model systems. It is similar Pytest, but is specialized for unit-testing LLM outputs. DeepEval incorporates research to evaluate LLM results based on metrics like G-Eval (hallucination), answer relevancy, RAGAS etc. This uses LLMs as well as various other NLP models which run locally on your computer for evaluation. DeepEval can handle any implementation, whether it's RAG, fine-tuning or LangChain or LlamaIndex. It allows you to easily determine the best hyperparameters for your RAG pipeline. You can also prevent drifting and even migrate from OpenAI to your own Llama2 without any worries. The framework integrates seamlessly with popular frameworks and supports synthetic dataset generation using advanced evolution techniques. It also allows for efficient benchmarking and optimizing of LLM systems.

Ragas is a framework that allows you to test and evaluate applications that use the Large Language Model. It provides automatic metrics for assessing performance and robustness. Synthetic test data is generated according to specific requirements. Workflows are also available to ensure quality in development and production monitoring. Ragas integrates seamlessly into existing stacks and provides insights to enhance LLM application. The platform is maintained and developed by a passionate team of individuals who use cutting-edge engineering practices and cutting-edge research to empower visionaries to redefine LLM possibilities. Synthesize high-quality, diverse evaluation data tailored to your needs. Evaluation and quality assurance of your LLM application during production. Use insights to improve the application. Automatic metrics to help you understand performance and robustness of the LLM application.

Manage and optimize models throughout the entire ML lifecycle. This includes experiment tracking, monitoring production models, and more. The platform was designed to meet the demands of large enterprise teams that deploy ML at scale. It supports any deployment strategy, whether it is private cloud, hybrid, or on-premise servers. Add two lines of code into your notebook or script to start tracking your experiments. It works with any machine-learning library and for any task. To understand differences in model performance, you can easily compare code, hyperparameters and metrics. Monitor your models from training to production. You can get alerts when something is wrong and debug your model to fix it. You can increase productivity, collaboration, visibility, and visibility among data scientists, data science groups, and even business stakeholders.

AI engineering does not have to be a mystery. You can get full visibility using tools for tracing and evaluation, prompt management and more. HoneyHive is a platform for AI observability, evaluation and team collaboration that helps teams build reliable generative AI applications. It provides tools for evaluating and testing AI models and monitoring them, allowing engineers, product managers and domain experts to work together effectively. Measure the quality of large test suites in order to identify improvements and regressions at each iteration. Track usage, feedback and quality at a large scale to identify issues and drive continuous improvements. HoneyHive offers flexibility and scalability for diverse organizational needs. It supports integration with different model providers and frameworks. It is ideal for teams who want to ensure the performance and quality of their AI agents. It provides a unified platform that allows for evaluation, monitoring and prompt management.

Software testing is based on the assumption that a system is predictable. AI systems are unpredictable and uncertain, creating risk for AI products. To reduce this risk, we're building a proactive AI evaluation and testing platform to make AI robust, safe, and reliable. Trust your AI before shipping it and continue to do so. We are iterating quickly to design the most comprehensive enterprise AI testing platform. We would love to hear your feedback. Sign up to get early versions of our product and help us shape its direction. We are a team of passionate individuals who are deeply focused on solving AI testing problems at the enterprise level. We are inspired by our customers, partners and advisors. As AI's capabilities across enterprise tasks grow, so do the potential risks to businesses and their customers. Every day, there are new reports of AI bias, instabilities, failures, errors, or other issues.

Literal AI is an open-source platform that helps engineering and product teams develop production-grade Large Language Model applications. It provides a suite for observability and evaluation, as well as analytics. This allows for efficient tracking, optimization and integration of prompt version. The key features are multimodal logging encompassing audio, video, and vision, prompt management, with versioning and testing capabilities, as well as a prompt playground to test multiple LLM providers. Literal AI integrates seamlessly into various LLM frameworks and AI providers, including OpenAI, LangChain and LlamaIndex. It also provides SDKs for Python and TypeScript to instrument code. The platform supports the creation and execution of experiments against datasets to facilitate continuous improvement in LLM applications.

Selenium tests can be unstable and difficult to maintain. Parasoft Selenic fixes Selenium issues within your existing projects without vendor lock. You need to be confident that your team's testing process is identifying and resolving real issues, creating meaningful tests, and reducing maintenance when your team uses Selenium for developing and testing the UI of your software applications. You want to maximize your UI testing, while leveraging the benefits of Selenium. Parasoft Selenic helps you find the real UI problems and receive quick feedback on test execution, so that you can deliver better software quicker. With a flexible Selenium partner that integrates seamlessly into your environment, you can improve your existing library of Selenium Web UI tests or quickly create new ones. Parasoft Selenic fixes Selenium issues with AI-powered self healing to minimize runtime errors, test impact analyses to reduce test execution times, etc.

Traceloop is an observability platform that allows you to monitor, debug and test the output quality from Large Language Models. It provides real-time alerts when unexpected output quality changes occur, execution tracing of every request and the ability to roll out changes to prompts and models in a gradual manner. Developers can debug issues directly from production in their Integrated Development Environment. Traceloop integrates seamlessly with the OpenLLMetry SDK, supporting multiple programming languages including Python, JavaScript/TypeScript, Go, and Ruby. The platform offers a wide range of semantic, syntax, safety and structural metrics for assessing LLM outputs. These include QA relevance, faithfulness and text quality. It also includes redundancy detection and focus assessment.

Early is an AI tool that automates the generation and maintenance unit tests. It improves code quality and accelerates development processes. Early integrates with Visual Studio Code to allow developers to create verified and validated tests directly from the codebase. This covers a variety of scenarios including edge cases and happy paths. This approach increases code coverage and helps identify potential issues earlier in the development cycle. Early supports TypeScript and JavaScript languages and is compatible with test frameworks like Jest and Mocha. The tool provides a seamless experience, allowing users to quickly access generated tests and refine them to meet specific requirements. Early automates the testing process to reduce the impact on bugs, prevent code regressions, and boost development speed, ultimately leading to a release of better-quality software.

MAIHEM creates AI Agents that continuously test your AI Applications. We automate AI quality assurance to ensure AI performance and safety, from development to deployment. Avoid hours of manual testing, and random probing for AI weaknesses. MAIHEM automates AI quality assurance, and covers thousands of edge cases. Create thousands of realistic personas that interact with your conversational artificial intelligence. Automatically evaluate conversations using customizable performance and risk metrics. Use the simulation data to improve your conversational AI. MAIHEM is able to improve the performance of any conversational AI application. With a few lines code, you can integrate AI quality assurance into your developer workflow. Web app with dashboards that offer AI quality assurance within a few clicks.

One intelligent functional testing tool that accelerates test automation for web, mobile and enterprise apps. Intelligent test automation that uses embedded AI-based capabilities to accelerate testing across desktop, mobile, mainframe and composite platforms. A single intelligent testing tool automates and accelerates the testing of more than 200 enterprise apps, technologies, and environments. AI-powered intelligent testing automation reduces the time and effort required to create functional tests and maintain them. It also increases test coverage and resilience. To increase coverage across the UI/API, test both the front-end functionality as well as the back-end service components of an application. Parallel testing, cross-browser coverage and cloud-based deployment allow you to test more quickly and get your tests executed at full speed.

Confident AI is used by companies of all sizes to prove that their LLM is worth being in production. On a single, central platform, you can evaluate your LLM workflow. Deploy LLM with confidence to ensure substantial benefits, and address any weaknesses within your LLM implementation. Provide ground truths to serve as benchmarks for evaluating your LLM stack. Ensure alignment with predefined output expectation, while identifying areas that need immediate refinement and adjustments. Define ground facts to ensure that your LLM behaves as expected. Advanced diff tracking for iterating towards the optimal LLM stack. We guide you through the process of selecting the right knowledge bases, altering the prompt templates and selecting the best configurations for your use case. Comprehensive analytics to identify focus areas. Use out-of-the box observability to identify use cases that will bring the greatest ROI for your organization. Use metric insights to reduce LLM costs and delays over time.

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.

The AI test engineer from BlinqIO is a test automation engineer that works just like a real person. It receives test descriptions or scenarios, determines how to execute them on the application or website being tested, and then creates test automation codes that can be pushed in your CICD system just like any other test automaton code. AI test engineers will fix code when the UI or flow of the application changes. Software release with zero risk is now possible thanks to unlimited 24/7 capacity. Automated test creation. Test automation scripts are created automatically. Executes and debugs the test scripts. Opens a task in the task management system and assigns it to RnD. Maintains and fixes the code of test scripts that failed because UI changes were made. Automatically performs the task by navigating through and interacting with application under test.

Pezzo is an open-source LLMOps tool for developers and teams. With just two lines of code you can monitor and troubleshoot your AI operations. You can also collaborate and manage all your prompts from one place.

A unified platform for LLM applications. Use all the best-in class LLMs. Integration is dead simple. You can easily trace user sessions, debug and trace user sessions.

ChainForge is a visual programming environment that is open-source and designed for large language model evaluation. It allows users to evaluate the robustness and accuracy of text-generation models and prompts beyond anecdotal data. Test prompt ideas and variations simultaneously across multiple LLMs in order to identify the most efficient combinations. Evaluate response quality for different prompts, models and settings to determine the optimal configuration. Set up evaluation metrics, and visualize results for prompts, parameters and models. This will facilitate data-driven decisions. Manage multiple conversations at once, template follow-ups, and inspect the outputs to refine interactions. ChainForge supports a variety of model providers including OpenAI HuggingFace Anthropic Google PaLM2, Azure OpenAI Endpoints and locally hosted models such as Alpaca and Llama. Users can modify model settings and use visualization nodes.

GoCodeo is the future of AI powered unit testing. We detect and eliminate bugs in software early on in the development cycle by leveraging a large ensemble of language models. GoCodeo provides developers with autonomous bug detection and code correction to release code confidently. Post-production bugs are a thing the past with GoCodeo. Say goodbye to manually writing tests and to the need for prompts in order to generate test code. GoCodeo AI will generate relevant, real-time test codes while you code. Instantly run test cases with a single click and find out the underlying causes of failures. We will provide you with code correction suggestions. You can then review and implement the fixes. AI simplifies regression testing. Update your codebase and avoid introducing new bugs. Code health metrics and reports on coverage provide deep insights and allow for value assessment. Encryption and compliance ensure that your code is kept in pristine condition. Cloud solutions are available for businesses.

To detect and respond to data drift, track model performance for better business outcomes. Arthur's transparency and explainability APIs help to build trust and ensure compliance. Monitor for bias and track model outcomes against custom bias metrics to improve the fairness of your models. {See how each model treats different population groups, proactively identify bias, and use Arthur's proprietary bias mitigation techniques.|Arthur's proprietary techniques for reducing bias can be used to identify bias in models and help you to see how they treat different populations.} {Arthur scales up and down to ingest up to 1MM transactions per second and deliver insights quickly.|Arthur can scale up and down to ingest as many transactions per second as possible and delivers insights quickly.} Only authorized users can perform actions. Each team/department can have their own environments with different access controls. Once data is ingested, it cannot be modified. This prevents manipulation of metrics/insights.

Scale Evaluation is a comprehensive evaluation tool for large language models. This platform addresses the current challenges in AI models assessment, including the scarcity and lack of high-quality evaluation datasets. Scale provides proprietary evaluation sets that cover a wide range of domains and capabilities. This ensures accurate model assessment without overfitting. The platform has a user-friendly interface that allows for the analysis and reporting of model performance. This allows for apples-to-apples comparisons. Scale's expert network of human raters also delivers reliable evaluations. This is supported by transparent metrics, and quality assurance mechanisms. The platform offers customized evaluations that focus on specific model concerns. This allows for precise improvements by using new training data.

Weights & Biases allows for experiment tracking, hyperparameter optimization and model and dataset versioning. With just 5 lines of code, you can track, compare, and visualise ML experiments. Add a few lines of code to your script and you'll be able to see live updates to your dashboard each time you train a different version of your model. Our hyperparameter search tool is scalable to a massive scale, allowing you to optimize models. Sweeps plug into your existing infrastructure and are lightweight. Save all the details of your machine learning pipeline, including data preparation, data versions, training and evaluation. It's easier than ever to share project updates. Add experiment logging to your script in a matter of minutes. Our lightweight integration is compatible with any Python script. W&B Weave helps developers build and iterate their AI applications with confidence.

Models can be opaque about what data they failed to perform well on and why. Galileo offers a variety of tools that allow ML teams to quickly inspect and find ML errors up to 10x faster. Galileo automatically analyzes your unlabeled data and identifies data gaps in your model. We get it - ML experimentation can be messy. It requires a lot data and model changes across many runs. You can track and compare your runs from one place. You can also quickly share reports with your entire team. Galileo is designed to integrate with your ML ecosystem. To retrain, send a fixed dataset to the data store, label mislabeled data to your labels, share a collaboration report, and much more, Galileo was designed for ML teams, enabling them to create better quality models faster.

TruLens, an open-source Python Library, is designed to evaluate and track Large Language Model applications. It offers fine-grained instruments, feedback functions and a user-interface to compare and iterate app versions. This facilitates rapid development and improvement of LLM based applications. Tools that allow scalable evaluation of the inputs, outputs and intermediate results of LLM applications. Instrumentation that is fine-grained and stack-agnostic, and comprehensive evaluations can help identify failure modes. A simple interface allows developers to compare versions of their application, facilitating informed decisions and optimization. TruLens supports a variety of use cases, such as question-answering and summarization. It also supports retrieval-augmented generation and agent-based apps.

AgentBench is a framework for evaluating the performance and capabilities of autonomous AI agents. It provides a set of benchmarks to test different aspects of an agent’s behavior such as task-solving, decision-making and adaptability. AgentBench evaluates agents on tasks in different domains to identify strengths and weakness. For example, the ability of agents to plan, reason and learn from feedback. The framework provides insights into how an agent can handle real-world scenarios that are complex. It is useful for both research as well as practical development. AgentBench is a tool that helps improve autonomous agents iteratively, ensuring that they meet standards of reliability and efficiency before being used in larger applications.

LangWatch is a vital part of AI maintenance. It protects you and your company from exposing sensitive information, prevents prompt injection, and keeps your AI on track, preventing unforeseen damage to the brand. Businesses with integrated AI can find it difficult to understand the behaviour of AI and users. Maintaining quality by monitoring will ensure accurate and appropriate responses. LangWatch's safety check and guardrails help prevent common AI problems, such as jailbreaking, exposing sensitive information, and off-topic discussions. Real-time metrics allow you to track conversion rates, output, user feedback, and knowledge base gaps. Gain constant insights for continuous improvements. Data evaluation tools allow you to test new models and prompts and run simulations.

Use Prompt mixer to create chains and prompts. Combine your chains with data sets and improve using AI. Test scenarios can be developed to evaluate various prompt and model combinations, determining the best combination for different use cases. Prompt mixer can be used for a variety of tasks, including creating content and conducting R&D. Prompt mixer can boost your productivity and streamline your workflow. Use Prompt mixer to create, evaluate, and deploy content models for different applications, such as emails and blog posts. Use Prompt mixer to extract or combine data in a secure manner, and monitor it easily after deployment.

A platform powered by AI and built on large language models (LLMs). Unique approach to creating, debugging, and evolving end-to-end tests using natural language. Intelligent automation simplifies the testing process by allowing for test generation and evolution using natural language inputs. Intelligent test planner automates and generates test steps based on high-level objectives. Multi-language code conversion converts automated tests into all major languages and frameworks. Convert your actions to natural language instructions for bulletproof tests. Natural language is the best way to express complex conditions and assertions. It's as easy as talking to your team. KaneAI will automate your tests if you give it the same instructions. Create your tests using only high-level objectives. Test your stack across web and mobile devices to ensure comprehensive coverage.

AI is today blocked by a lack of labeled data. Not models. The first data-centric AI platform powered by a programmatic approach will unblock AI. With its unique programmatic approach, Snorkel AI is leading a shift from model-centric AI development to data-centric AI. By replacing manual labeling with programmatic labeling, you can save time and money. You can quickly adapt to changing data and business goals by changing code rather than manually re-labeling entire datasets. Rapid, guided iteration of the training data is required to develop and deploy AI models of high quality. Versioning and auditing data like code leads to faster and more ethical deployments. By collaborating on a common interface, which provides the data necessary to train models, subject matter experts can be integrated. Reduce risk and ensure compliance by labeling programmatically, and not sending data to external annotators.

Reliv automates QA without a single line code. Click the recording button, and then follow the scenario in your browser. The actions will automatically be recognized and a test created. You can run your test with just one click. You can now check the results at once. Tests can be run daily or before deployment. Anyone in your team can easily edit and create tests. Invite your teammates to participate in the test management. Write in plain text and the AI will take care of the rest. AI will handle the rest. Just describe the actions that you want. You don't need to manually test every deployment. Automate critical scenarios to prevent serious bugs. It's 10x faster than using frameworks such as Selenium to automate. You can run as many tests as needed without any additional fees. Run tests regularly to monitor your service status at any time.

AI-powered web app testing tool that finds bugs before users. We only require your website URL. Our AI agent knows exactly what to test and writes the tests. It also keeps them up-to-date. You can run the tests directly from our app, or integrate them into your CI/CD pipeline. End-to-end testing has a serious trust issue. Test runs failing is not only due to broken code. Third-party dependencies, timing issues, randomness, race conditions and leak states make tests unreliable. We're deploying mitigation techniques so you don't waste time debugging perfectly good code.

Qualisense Test.Predictor, our AI-powered tool, dramatically improves risk-based testing strategies. It uses AI and automation to accelerate time to release, reduce costs, and redeploy resources so that you can focus on the most important things. You can dramatically increase speed to market by achieving a 6X increase in release velocity. When it comes to Test.Predictor it's not just a slogan. It's a way of operating. These AI capabilities are revolutionizing software testing and redefining regression testing. Test.Predictor allows data analysts and business users to create their own prediction models. It's the ultimate testing tool.

PARASOFT SOATEST Artificial Intelligence and Machine Learning Power APIs and Web Service Testing Tools Parasoft SOAtest is based on artificial intelligence (AI), machine learning (ML), and simplifies functional testing across APIs and UIs. The API and web service testing tool is perfect for Agile DevOps environments because it uses continuous quality monitoring systems to monitor the quality of change management systems. Parasoft SOAtest is a fully integrated API and web-service testing tool that automates end-to-end functional API test automation. Automated testing is simplified with advanced functional test-creation capabilities. This applies to applications with multiple interfaces (REST and SOAP APIs as well as microservices, databases, etc.). These tools reduce security breaches and performance issues by turning functional testing artifacts in security and load equivalents. This allows for faster and more efficient testing, while also allowing continuous monitoring of API changes.

BugBot is a revolutionary exploratory testing tool that seamlessly integrates generative AI, automation and visual testing. Screen recording and comprehensive automation allows you to validate forms quickly. You can also record and import test sessions. BugBot provides 30-40% savings in time, 70 percent reduction in manual intervention and 60 to 70% accuracy for specific environments. BugBot is a leader in the industry, with its powerful AI-enabled testing and integrations. It offers 4X faster delivery and execution. Choose BugBot to get instant testing support, cross-platform compatibility and 24/7 service assistance.

You can test the new functionality of your app by interacting with mobile devices from your browser. You can create and execute hundreds of manual and automated tests on more than 1,000 Android and IOS devices in the cloud. Appium tests can be created directly from your IDE. You can also interact with the appium team and debug live. Advanced analytics and visual test reports are available. Selenium runs Selenium tests on more than 1,000 browser types, versions, operating systems, and other devices to automate cross-browser testing. You can interact with your app in real time and debug it. Visual testing is used to verify UI responsiveness at different resolutions. Appium Studio allows you to intuitively create new Appium tests and execute existing projects. You can easily test iOS devices on a Windows machine and enjoy advanced testing capabilities. Digital.ai Continuous testing allows enterprises to test at scale, increase coverage, and make data-driven choices to deliver high-quality apps.