Alternatives to Keywords AI

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.

Faros AI combines all your operational data from multiple sources and enhances them with machine learning signals. The Faros AI Engineering Operations Platform allows you to harness this data so you can accelerate productivity, and better manager your engineering operations. With Faros AI, engineering leaders can scale their operations in a more data-informed way — using data to identify bottlenecks, measure progress towards organizational goals, better support teams with the right resources, and accurately assess the impact of interventions over time. DORA Metrics come standard in Faros AI, and the platform is extensible to allow organizations to build their own custom dashboards and metrics so they can get deep insights into their engineering operations and take intelligent action in a data-driven manner. Leading organizations including Box, Coursera, GoFundMe, Astronomer, Salesforce, etc. trust Faros AI as their engops platform of choice.

Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness.

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.

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!

It's not enough to just look at a few examples. To get actionable insights about how to improve your models, gather feedback from end-users at large. With the GPT improvement engine, you can easily A/B test models. You can only go so far with prompts. Fine-tuning your best data will produce better results. No coding or data science required. Integration in one line of code You can experiment with ChatGPT, Claude and other language model providers without having to touch it again. If you have the right tools to customize models for your customers, you can build innovative and defensible products on top APIs. Copy AI allows you to fine tune models based on the best data. This will allow you to save money and give you a competitive edge. This technology allows for magical product experiences that delight more than 2 million users.

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.

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.

ClearML is an open-source MLOps platform that enables data scientists, ML engineers, and DevOps to easily create, orchestrate and automate ML processes at scale. Our frictionless and unified end-to-end MLOps Suite allows users and customers to concentrate on developing ML code and automating their workflows. ClearML is used to develop a highly reproducible process for end-to-end AI models lifecycles by more than 1,300 enterprises, from product feature discovery to model deployment and production monitoring. You can use all of our modules to create a complete ecosystem, or you can plug in your existing tools and start using them. ClearML is trusted worldwide by more than 150,000 Data Scientists, Data Engineers and ML Engineers at Fortune 500 companies, enterprises and innovative start-ups.

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

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.

RagaAI is a leading AI testing platform which helps enterprises to mitigate AI risks, and make their models reliable and secure. Intelligent recommendations will reduce AI risk across cloud or edge deployments, and optimize MLOps cost. A foundation model designed specifically to revolutionize AI testing. You can easily identify the next steps for fixing dataset and model problems. AI-testing methods are used by many today, and they increase time commitments and reduce productivity when building models. They also leave unforeseen risks and perform poorly after deployment, wasting both time and money. We have created an end-toend AI testing platform to help enterprises improve their AI pipeline and prevent inefficiencies. 300+ tests to identify, fix, and accelerate AI development by identifying and fixing every model, data and operational issue.

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.

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.

Our dashboard allows you to dig deeper into analytics, allowing you to verify the information you need to enter requests into Guardrails. Our library of ready-to-use validators will help you unlock efficiency. Validation for diverse use cases can optimize your workflow. Boost your projects by leveraging a dynamic framework that allows you to create, manage, and reuse custom validators. The versatility of the software is matched by its ease of use, allowing it to be used for a wide range innovative applications. You can quickly generate another output option by indicating the error and verifying it. Assures that the outcomes are in line, with expectations, accuracy, correctness, reliability, and interactions with LLMs.

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.

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.

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.

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.

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.

Phoenix is a free, open-source library for observability. It was designed to be used for experimentation, evaluation and troubleshooting. It allows AI engineers to visualize their data quickly, evaluate performance, track issues, and export the data to improve. Phoenix was built by Arize AI and a group of core contributors. Arize AI is the company behind AI Observability Platform, an industry-leading AI platform. Phoenix uses OpenTelemetry, OpenInference, and other instrumentation. The main Phoenix package arize-phoenix. We offer a variety of helper packages to suit specific use cases. Our semantic layer adds LLM telemetry into OpenTelemetry. Automatically instrumenting popular package. Phoenix's open source library supports tracing AI applications via manual instrumentation, or through integrations LlamaIndex Langchain OpenAI and others. LLM tracing records requests' paths as they propagate across multiple steps or components in an LLM application.

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.

Velocity provides detailed, contextual analytics that enable engineering leaders to help their team members, resolve team roadblocks and streamline engineering processes. Engineering leaders can get actionable metrics. Velocity transforms data from commits to pull requests into the insights that you need to make lasting improvements in your team's productivity. Quality: Automated code reviews for test coverage, maintainability, and more so you can save time and merge with confidence. Automated code review comments for pull requests. Our 10-point technical debt assessment gives you real-time feedback so that you can focus on the important things in your code review discussions. You can get perfect coverage every time. Check coverage line-by-line within diffs. Never merge code again without passing sufficient tests. You can quickly identify files that are frequently modified and have poor coverage or maintainability issues. Each day, track your progress towards measurable goals.

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.

Compose, test and optimize prompts for the most popular language models and AI platforms. Promptmetheus, an Integrated Development Environment for LLM prompts is designed to help automate workflows and enhance products and services using the mighty GPT and other cutting edge AI models. The transformer architecture has enabled cutting-edge Language Models to reach parity with the human ability in certain narrow cognitive tasks. To effectively leverage their power, however, we must ask the right questions. Promptmetheus is a complete prompt engineering software toolkit that adds composability and traceability to the prompt design to help you discover those questions.

Dynamiq was built for engineers and data scientist to build, deploy and test Large Language Models, and to monitor and fine tune them for any enterprise use case. Key Features: Workflows: Create GenAI workflows using a low-code interface for automating tasks at scale Knowledge & RAG - Create custom RAG knowledge bases in minutes and deploy vector DBs Agents Ops - Create custom LLM agents for complex tasks and connect them to internal APIs Observability: Logging all interactions and using large-scale LLM evaluations of quality Guardrails: Accurate and reliable LLM outputs, with pre-built validators and detection of sensitive content. Fine-tuning : Customize proprietary LLM models by fine-tuning them to your liking

Salesforce's first DevOps Value Stream Platform. Learn more about Copado's transformative Winter '21 Release. Copado DevOps brings continuous value to your business's bottom line through your cloud platform. Create release pipelines to deploy Salesforce metadata, and seamlessly sync all of your orgs. With user stories, epics, and integrations with Jira, Azure DevOps, Jira, and Jira, you can simplify sprint and feature planning. To improve quality and ensure compliance, you can leverage the built-in quality gates as well as testing automation. All this is possible on the trusted, secure Salesforce Platform. DevOps 360 Analytics allows you to measure and monitor your development and help you improve your agile adoption and processes through Value Stream Maps. Our flexible architecture lets you work with the version control and ALM tools you already use. Teams can see the benefits of Native DevOps Solutions for Salesforce in weeks, not years.

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.

Platform for AI agents testing and debugging by the industry's leading developers. We developed the tools, so you don't need to. Visually track events, such as LLM, tools, and agent interactions. Rewind and playback agent runs with pinpoint precision. Keep a complete data trail from prototype to production of logs, errors and prompt injection attacks. Native integrations with top agent frameworks. Track, save and monitor each token that your agent sees. Monitor and manage agent spending using the most recent price monitoring. Save up to 25x on specialized LLMs by fine-tuning them based on completed completions. Build your next agent using evals and replays. You can visualize the behavior of your agents in your AgentOps dashboard with just two lines of coding. After you set up AgentOps each execution of your program will be recorded as a "session" and the data will automatically be recorded for you.

Maxim is a enterprise-grade stack that enables AI teams to build applications with speed, reliability, and quality. Bring the best practices from traditional software development to your non-deterministic AI work flows. Playground for your rapid engineering needs. Iterate quickly and systematically with your team. Organise and version prompts away from the codebase. Test, iterate and deploy prompts with no code changes. Connect to your data, RAG Pipelines, and prompt tools. Chain prompts, other components and workflows together to create and test workflows. Unified framework for machine- and human-evaluation. Quantify improvements and regressions to deploy with confidence. Visualize the evaluation of large test suites and multiple versions. Simplify and scale human assessment pipelines. Integrate seamlessly into your CI/CD workflows. Monitor AI system usage in real-time and optimize it with speed.

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.

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.

MLflow is an open-source platform that manages the ML lifecycle. It includes experimentation, reproducibility and deployment. There is also a central model registry. MLflow currently has four components. Record and query experiments: data, code, config, results. Data science code can be packaged in a format that can be reproduced on any platform. Machine learning models can be deployed in a variety of environments. A central repository can store, annotate and discover models, as well as manage them. The MLflow Tracking component provides an API and UI to log parameters, code versions and metrics. It can also be used to visualize the results later. MLflow Tracking allows you to log and query experiments using Python REST, R API, Java API APIs, and REST. An MLflow Project is a way to package data science code in a reusable, reproducible manner. It is based primarily upon conventions. The Projects component also includes an API and command line tools to run projects.

The open-source ML observability Platform. From validation to production, evaluate, test, and track ML models. From tabular data up to NLP and LLM. Built for data scientists and ML Engineers. All you need to run ML systems reliably in production. Start with simple ad-hoc checks. Scale up to the full monitoring platform. All in one tool with consistent APIs and metrics. Useful, beautiful and shareable. Explore and debug a comprehensive view on data and ML models. Start in a matter of seconds. Test before shipping, validate in production, and run checks with every model update. By generating test conditions based on a reference dataset, you can skip the manual setup. Monitor all aspects of your data, models and test results. Proactively identify and resolve production model problems, ensure optimal performance and continually improve it.

The first platform designed for prompt engineers. Log OpenAI requests, track usage history, visual manage prompt templates, and track performance. Manage Never forget one good prompt. GPT in Prod, done right. Trusted by more than 1,000 engineers to monitor API usage and version prompts. Your prompts can be used in production. Click "log in" to create an account on PromptLayer. Once you have logged in, click on the button to create an API Key and save it in a secure place. After you have made your first few requests, the API key should be visible in the PromptLayer dashboard. LangChain can be used with PromptLayer. LangChain is a popular Python library that assists in the development and maintenance of LLM applications. It offers many useful features such as memory, agents, chains, and agents. Our Python wrapper library, which can be installed with pip, is the best way to access PromptLayer at this time.

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.

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.

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.

We'll take care of the rest. From deployment to elastic scaling, just write normal async codes. No timeouts and real-time monitoring. Zero infrastructure to manage. Trigger.dev, an open-source platform and SDK, allows developers to create background jobs that run for a long time without timeouts directly from their existing codebase. It supports JavaScript and TypeScript to allow for the writing of reliable, asynchronous code which integrates seamlessly into existing workflows. The platform provides features like API integrations and webhooks. It also offers scheduling, delays, and concurrency control without the need for servers. Trigger.dev has built-in monitoring tools that include real-time updates on run status, advanced filtering and custom alerts sent via email, Slack or webhooks. Its architecture allows for elastic scaling, which is essential to efficiently handle varying workloads. Developers can deploy tasks via a command-line, while the platform handles scaling management.

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.

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.

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.

Levity is a no-code platform for creating custom AI models that take daily, repetitive tasks off your shoulders. Levity allows you to train AI models on documents, free text or images without writing any code. Build intelligent automations into existing workflows and connect them to the tools you already use. The platform is designed in a non-technical way, so everybody can start building within minutes and set up powerful automations without waiting for developer resources. If you struggle with daily tedious tasks that rule-based automation just can't handle, Levity is the quickest way to finally let machines handle them. Check out Levity's extensive library of templates for common use-cases such as sentiment analysis, customer support or document classification to get started within minutes. Add your custom data to further tailor the AI to your specific needs and only stay in the loop for difficult cases, so the AI can learn along the way.

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

Everything you need to turn your ideas into AI apps within minutes. Our platform is easy to use and anyone can create next-generation AI-powered applications in just 10 minutes. Integrate APIs such as ChatGPT, Dalle-2 and Codex from Open AI seamlessly and even create custom ML models. To manage inventory, sales, contracts, and other functions, you can create a custom admin client or CRUD functionality. Dynamic dashboards can be created to transform data into actionable insights, and drive innovation for your business. Create a chatbot with multiple integrations to improve customer service and create an omnichannel experience. All-in-one cloud platform that combines low-code/no code tools with edge technologies. This makes your web application easy to manage, secure, and scalable. Our no-code/low code platform is perfect for both professional and business developers.

Aicado is the platform of choice for AI solutions that don't require any coding. Select your favorite AI model, customize to your needs, then seamlessly integrate it into the business. We have the AI models that match. You can explore them, test them, and integrate them into the business for free. Aicado allows you to create an unlimited number of integrations. Every model can be integrated, and every integration can serve different purposes and connect to different domains. Faces can be easily swapped in videos with high precision. Ideal for creating creative and fun edits. Speak or upload a recording. Type a few words to describe the scene or object that you are picturing, and the AI will do the rest. It will then present you with a visual representation. Enter the text and the AI will take care of the rest. You can listen to the audio version. You can even turn your blog into a podcast.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

PlugBear provides a low-code/no-code solution to connect communication channels with LLM applications (Large Language Model). It allows, for example, the creation of a Slack Bot from an LLM application in just a few simple clicks. PlugBear is notified when a trigger event occurs on the integrated channels. It then transforms messages into LLM applications, and initiates generation. PlugBear then transforms the generated results so that they are compatible with each channel. This allows users to interact with LLM applications seamlessly across different channels.

Transform your business operations with our cutting-edge AI-powered solutions. Harness the potential of historical data to drive innovation, optimize efficiency, and propel your growth – revolutionizing your business processes, one task at a time. At Ever Efficient AI, we understand the value of your historical data and its untapped potential. By analyzing historical data in new and creative ways, we unlock opportunities for process efficiency, enhanced decision-making, waste reduction & drive growth. Ever Efficient AI's task automation is designed to take the strain out of your daily operations. Our AI systems can manage and automate a wide range of tasks, from scheduling to data management, allowing you and your team to focus on what truly matters - your core business.