Alternatives to Omni AI

AI agents that interact and answer questions with users and complete tasks using your data and APIs. AI that can answer questions, summarize and extract insights from any long document. Transfer styles and formats, as well as tags and summaries between documents and data sources. Stack AI is used by developer teams to automate customer service, process documents, qualify leads, and search libraries of data. With a single button, you can try multiple LLM architectures and prompts. Collect data, run fine-tuning tasks and build the optimal LLM to fit your product. We host your workflows in APIs, so that your users have access to AI instantly. Compare the fine-tuning services of different LLM providers.

Wordware allows anyone to create, iterate and deploy useful AI agents. Wordware combines software's best features with the power of language. Remove the constraints of traditional tools that don't require code and empower each team member to iterate on their own. Natural language programming will be around for a long time. Wordware removes prompt from codebases by providing non-technical and technical users with a powerful AI agent creation IDE. Our interface is simple and flexible. With an intuitive design, you can empower your team to collaborate easily, manage prompts and streamline workflows. Loops, branching and structured generation, as well as version control and type safety, help you make the most of LLMs. Custom code execution allows you connect to any API. Switch between large language models with just one click. Optimize your workflows with the best cost-to-latency-to-quality ratios for your application.

Flowise is open source and will always be free to use for commercial and private purposes. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT License, see your LLM applications running live, and manage component integrations. GitHub Q&A using conversational retrieval QA chains. Language translation using LLM chains with a chat model and chat prompt template. Conversational agent for chat model that uses chat-specific prompts.

Lunary is a platform for AI developers that helps AI teams to manage, improve and protect chatbots based on Large Language Models (LLM). It includes features like conversation and feedback tracking as well as analytics on costs and performance. There are also debugging tools and a prompt directory to facilitate team collaboration and versioning. Lunary integrates with various LLMs, frameworks, and languages, including OpenAI, LangChain and JavaScript, and offers SDKs in Python and JavaScript. Guardrails to prevent malicious prompts or sensitive data leaks. Deploy Kubernetes/Docker in your VPC. Your team can judge the responses of your LLMs. Learn what languages your users speak. Experiment with LLM models and prompts. Search and filter everything in milliseconds. Receive notifications when agents do not perform as expected. Lunary's core technology is 100% open source. Start in minutes, whether you want to self-host or use the cloud.

Composio is a platform for integration that enhances AI agents and Large Language Models by providing seamless connections with over 150 tools. It supports a variety of agentic frameworks, LLM providers and function calling for efficient task completion. Composio provides a wide range of tools including GitHub and Salesforce, file management and code execution environments. This allows AI agents to perform a variety of actions and subscribe to different triggers. The platform offers managed authentication that allows users to manage authentication processes for users and agents through a central dashboard. Composio's core features include a developer first integration approach, built in authentication management, and an expanding catalog with over 90 ready to connect tools. It also includes a 30% reliability increase through simplified JSON structure and improved error handling.

Import data, such as text or images, from any source, including documents, cloud services or APIs, and launch a ChatGPT interface based upon your preferred large-language model, like GPT-4 or FLAN, and answer user questions based on imported data. A comprehensive list of sample queries that can be sent to an LLM connected through ZBrain to a company’s private data source. ZBrain can be seamlessly integrated into your existing products and tools as a prompt response service. You can enhance your deployment experience by choosing secure options such as ZBrain Cloud, or self-hosting on a private infrastructure. ZBrain Flow allows you to create business rules without writing code. The intuitive flow interface lets you connect multiple large language and prompt templates, image and video models, and extraction and parsing to build powerful, intelligent applications.

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.

PromptQL, a platform created by Hasura, allows Large Language Models to interact with structured data through agentic querying. This approach allows AI agents retrieve and process data using a human-like interface, improving their ability to handle real-world queries. PromptQL allows LLMs to manipulate and query data accurately by providing them with a Python interface and a standard SQL interface. The platform allows users to create AI assistants that are tailored to their needs by integrating with different data sources such as GitHub repositories or PostgreSQL database. PromptQL overcomes the limitations of traditional search retrieval methods, allowing AI agents to perform tasks like gathering relevant emails and identifying follow-ups more accurately. Users can start by connecting their data, adding the LLM API key and building with AI.

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

MakerSuite simplifies this process. MakerSuite allows you to easily tune custom models, iterate on prompts and augment your data with synthetic data. MakerSuite allows you to export your prompts as code in your favorite languages, such as Python and Node.js, when you are ready to move on to code.

The easiest way to create serverless AI agents with memory. Start building agentic pipes and tools locally first. Deploy serverless in one command. Base AI allows developers to create high-quality AI agents that have memory (RAG) in TypeScript, and then deploy serverless using Langbase's (creators of Base AI) highly scalable API. Base AI is a web-first solution with TypeScript and a familiar API. You can integrate AI into your web stack with ease, using Next.js or Vue or vanilla Node.js. Base AI is a great tool for delivering AI features faster. Create AI features on-premises with no cloud costs. Git is integrated out of the box so you can branch AI models and merge them like code. Complete observability logs allow you to debug AI-like JavaScript and trace data points, decisions, and outputs. It's Chrome DevTools, but for AI.

A managed PaaS that includes a low-code visual editor, VectorDB and integrations with apps and models to build, test, and deploy high-performance AI applications on the edge. Eliminate costly and error-prone work. Drag and drop agents, apps, data and models to find the best solution. Deployment in less than 60 seconds, and a 50% reduction in latency. Observe, iterate, and test seamlessly. Visibility and tools are essential for accuracy and reliability. Use data-driven decision making with reports on usage, LLM and request. View real-time traces per node. Experiments allow you to optimize embeddings and prompts, models and more. All you need to launch and iterate at large scale. Community of smart-minded builders who share their insights, experiences & feedback. Distilling the most useful tips, tricks and techniques for AI application developers. A platform that allows you to build agentic systems as if you were a 100-person team. A simple and intuitive frontend for managing AI applications and collaborating with them.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

Our open-source research efforts are focused on both the new "ware" (middleware and "software" which will wrap and integrate LLMs) as well as building high quality, automation-focused enterprise model available in Hugging Face. LLMWare is also a coherent, high quality, integrated and organized framework for developing LLM-applications in an open system. This provides the foundation for creating LLM-applications that are designed for AI Agent workflows and Retrieval Augmented Generation. Our LLM framework was built from the ground-up to handle complex enterprise use cases. We can provide pre-built LLMs tailored to your industry, or we can fine-tune and customize an LLM for specific domains and use cases. We provide an end-toend solution, from a robust AI framework to specialized models.

With our cloud partners, you can build, train, and deploy your models in cloud using the most recent IPU AI systems and frameworks. This allows you to scale up to large IPU compute seamlessly, while saving on compute costs. Get started with IPUs today by getting on-demand pricing and tiers free of charge from our cloud partners. Our Intelligence Processing Unit (IPU), technology is expected to become the global standard for machine intelligence computing. The Graphcore IPU will have a transformative impact across all industries and sectors. It has the potential to have a real positive societal impact, from drug discovery to disaster recovery to decarbonization. The IPU is an entirely new processor that was specifically designed for AI computation. AI researchers can use the IPU's unique architecture to do completely new types of work that are not possible with current technologies. This will allow them to drive the next generation in machine intelligence.

Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40.

Tencent Cloud TI Platform, a machine learning platform for AI engineers, is a one stop shop. It supports AI development at every stage, from data preprocessing, to model building, to model training, to model evaluation, as well as model service. It is preconfigured with diverse algorithms components and supports multiple algorithm frameworks for adapting to different AI use-cases. Tencent Cloud TI Platform offers a machine learning experience in a single-stop shop. It covers a closed-loop workflow, from data preprocessing, to model building, training and evaluation. Tencent Cloud TI Platform allows even AI beginners to have their models automatically constructed, making the entire training process much easier. Tencent Cloud TI Platform’s auto-tuning feature can also improve the efficiency of parameter optimization. Tencent Cloud TI Platform enables CPU/GPU resources that can elastically respond with flexible billing methods to different computing power requirements.

Arches AI offers tools to create chatbots, train custom model, and generate AI-based content, all tailored to meet your specific needs. Deploy stable diffusion models, LLMs and more. A large language model agent (LLM) is a type artificial intelligence that uses deep-learning techniques and large data sets in order to understand, summarize and predict new content. Arches AI converts your documents into 'word embeddings.' These embeddings let you search by semantic meaning rather than by exact language. This is extremely useful when trying understand unstructured text information such as textbooks or documentation. Your information is protected from hackers and other bad characters by the strict security rules. You can delete all documents on the 'Files page'.

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.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

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.

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.

Google AI Studio is an online tool that's free and allows individuals and small groups to create apps and chatbots by using natural language prompting. It allows users to create API keys and prompts for app development. Google AI Studio allows users to discover Gemini Pro's APIs, create prompts and fine-tune Gemini. It also offers generous free quotas, allowing 60 requests a minute. Google has also developed a Generative AI Studio based on Vertex AI. It has models of various types that allow users to generate text, images, or audio content.

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.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

Test, launch and refine Gen AI prompting. RAG Agents. Datasets. Workflows. A single workspace for Engineers and Product Managers to explore LLM technology.

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.

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.

Powerful language models a click away. Join over 8,000 developers in building the next wave world-changing applications. Fine-tune GPT-J and deploy Codegen, FLAN-T5, GPT NeoX and GPT NeoX. There are multiple models with different capabilities and prices. GPT-J has the fastest speed, while GPT NeoX is the most powerful. And more models are coming. These models can be used for classification, entity extracting, code generation and chatbots. They can also be used for content generation, summarizations, paraphrasings, sentiment analysis and more. These models have already been pre-trained using a large amount of text taken from the internet. The fine-tuning process improves this for specific tasks, by training on more examples than are possible in a prompt. This allows you to achieve better results across a range of tasks.

Take control of your LLMs with Freeplay. It gives product teams the ability to prototype faster, test confidently, and optimize features. A better way to build using LLMs. Bridge the gap between domain specialists & developers. Engineering, testing & evaluation toolkits for your entire team.

Start building and deploying agents, retrieval-augmented generation and more in the cloud. We charge a flat rate per request. There are two types: document indexation, and generation. Document indexation is adding a document to the knowledge base. Document indexation is the addition a document to your Knowledge Base and generation, that creates LLM writing on your Knowledge Base RAG. Create a RAG workflow using off-the shelf components and prototype the system that best suits your case. We support many auxiliary functions, including reverse-tracing of output into documents and ingestion for a variety of file formats. Agents can be used to enable the LLM's use of tools. Agent-powered systems can decide what data they need and search for it. Our implementation of Agents provides a simple host for execution layers, and pre-built agents for many use scenarios.

Capture production data. Evaluate outputs together and fine-tune the performance of your LLM. A detailed log overview will help you understand what is happening in production. Work with domain experts, product managers and engineers to create reliable model outputs. Track AI metrics, such as speed, token usage, and quality scores. Copilot automates model evaluations and improvements for your use cases. Create, manage, or optimize prompts for precise and relevant interactions between AI models and users. Compare fine-tuned models and foundation models to improve prompt performance. Build a fine-tuning dataset with your team. Create custom fine-tuning data to optimize model performance.

Create generative AI apps for engineers and not just ML practitioners. Focus on creating instead of configuring. No more switching platforms or wrestling with APIs. Use a familiar interface for AI and to prompt engineers. Workbooks can be easily streamlined into templates by using parameters. Create workflows using model outputs from LLMs and image and audio models. Create groups to manage workbooks between your teammates. Share your workbook with your team or the public, or to specific organizations that you define. Workbooks can be commented on and compared with your team. Create templates for you, your team or the developer community. Get started quickly by using templates to see what others are building.

Get a complete picture of the performance of your model. Log inputs and out-puts, and enrich them with metadata. Find out what your model is doing and where it can be improved. Monitor for errors, and identify underperforming cohorts or use cases. The best models are based on user data. To retrain your model, you can programmatically gather examples that are unusual or underperforming. When changing your model or prompt, stop manually reviewing thousands outputs. Apps powered by LLM can be evaluated programmatically. Detect and fix degradations fast. Monitor new deployments and edit your app in real-time. Connect your data sources to your self-hosted model or third-party model. Our serverless streaming dataflow engines can handle large amounts of data. Gantry is SOC-2-compliant and built using enterprise-grade authentication.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

Laminar is a platform that allows you to create the best LLM products. The quality of your LLM application is determined by the data you collect. Laminar helps collect, understand, and use this data. You can collect valuable data and get a clear view of the execution of your LLM application by tracing it. You can use this data to create better evaluations, dynamic examples and fine-tune your application. All traces are sent via gRPC in the background with minimal overhead. Audio models will be supported soon. Tracing text and image models are supported. You can use LLM-as a judge or Python script evaluators on each span. Evaluators can label spans. This is more scalable than manual labeling and is especially useful for smaller teams. Laminar allows you to go beyond a simple prompt. You can create and host complex chains including mixtures of agents, or self-reflecting LLM pipes.

The first AI platform for business that does not require any code allows you to train and deploy GPT models in any use case. Our experts are ready to assist you. Our cost-effective tools can be used to fine-tune your open source models using your own data. You can deploy your open source model securely across multiple clouds, without having to rely on a third-party vendor for your valuable data. Our team of experts will create the perfect app for your needs. Our tool allows you to easily monitor your usage, and reduce costs. Let our team of experts help you solve your problems. Automate your customer service and efficiently classify your calls. Our advanced solution allows you to streamline customer interaction and improve service delivery. Build a robust system to detect fraud and anomalies based on previously flagged information.

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

With our enterprise Gen AI stack you can go beyond your imagination. You can instantly offload manual tasks and give them to powerful assistants. The sky is the limit. For enterprises that place data security first, fine-tune generative AI models and deploy them on your own cloud securely.

Query and compare multiple proprietary and open-source models simultaneously. Replace expensive APIs with custom AI models. Customize foundational AI models using your private data and adapt them to fit your specific use case. Small, fine-tuned models perform at the same level as GPT-4 while being up to 90% less expensive. Airtrain's LLM-assisted scoring simplifies model grading using your task descriptions. Airtrain's API allows you to serve your custom models in the cloud, or on your own secure infrastructure. Evaluate and compare proprietary and open-source models across your entire dataset using custom properties. Airtrain's powerful AI evaluation tools let you score models based on arbitrary properties to create a fully customized assessment. Find out which model produces outputs that are compliant with the JSON Schema required by your agents or applications. Your dataset is scored by models using metrics such as length and compression.

AutoTrain is a new way to automatically evaluate, deploy and train state-of-the art Machine Learning models. AutoTrain, seamlessly integrated into the Hugging Face ecosystem, is an automated way to develop and deploy state of-the-art Machine Learning model. Your account is protected from all data, including your training data. All data transfers are encrypted. Today's options include text classification, text scoring and entity recognition. Files in CSV, TSV, or JSON can be hosted anywhere. After training is completed, we delete all training data. Hugging Face also has an AI-generated content detection tool.

We offer a set of world-class tools and assets to convert ideas into production ready AI solutions. Build and deploy custom Generative AI end-to-end solutions on our unified Platform, and avoid the hassle of tool fragmentation or platform switching. Launch your next AI-based solution using a single API endpoint. It has never been easier to create, maintain, and improve AI systems. Subscribe to models and datasets on aiXplain’s marketplace. Subscribe to models and data sets to use with aiXplain's no-code/low code tools or the SDK.

Here is where the future of AI development begins. Modular is a composable, integrated suite of tools which simplifies your AI infrastructure, allowing your team to develop, deploy and innovate faster. Modular's inference engines unify AI industry frameworks with hardware. This allows you to deploy into any cloud or on-prem environments with minimal code changes, unlocking unmatched portability, performance and usability. Move your workloads seamlessly to the best hardware without rewriting your models or recompiling them. Avoid lock-in, and take advantage of cloud performance and price improvements without migration costs.

TorqCloud was designed to help users source data, move it, enrich it, visualize, secure and interact with that data using AI agents. TorqCloud is a comprehensive AIOps tool that allows users to create or integrate custom LLM applications end-to-end using a low code interface. Built to handle massive amounts of data and deliver actionable insights, TorqCloud is a vital tool for any organization that wants to stay competitive in the digital landscape. Our approach combines seamless interdisciplinarity, a focus on user needs, test and learn methodologies that allow us to get the product to market quickly, and a close relationship with your team, including skills transfers and training. We begin with empathy interviews and then perform stakeholder mapping exercises. This is where we explore the customer journey, behavioral changes needed, problem sizing and linear unpacking.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Use AI algorithms to receive intelligent suggestions about your API design. Automated recommendations tailored to your project will accelerate development. AI can automatically generate your database. Streamline deployment and increase productivity. Create and implement automated workflows to ensure a smooth, efficient development cycle. Customize automation processes to meet your project requirements. Workflows that are adaptable will allow you to create a personalized experience. Enjoy the flexibility to manage diverse data sources in a single, organized workspace. Workspaces can be designed to reflect the structure of projects. Create dedicated workspaces that can house multiple data sources seamlessly. Streamlining tasks by automating processes, increasing efficiency, and reducing the amount of manual effort. Each user has their own instance(s). Custom logic can be used to handle complex data operations.

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.

StartKit.AI was designed to accelerate the development of AI-based projects. It offers pre-built routes for all common AI tasks, including chat, images and long-form texts, as well as speech-to text, text-tospeech, translations and moderation. Also, more complex integrations such as web-crawlings, vector embeddings and more! It also comes with features for managing API limits and users, as well as a detailed documentation of all the code provided. Upon purchase, the customer receives access to the entire StartKit.AI GitHub repository, where they can customize and download the full code base. The code base includes 6 demo apps, which show you how to create your very own ChatGPT clone. This is the perfect starting point for creating your own app.

Emly Labs, an AI framework, is designed to make AI accessible to users of all technical levels via a user-friendly interface. It offers AI project-management with tools that automate workflows for faster execution. The platform promotes team collaboration, innovation, and data preparation without code. It also integrates external data to create robust AI models. Emly AutoML automates model evaluation and data processing, reducing the need for human input. It prioritizes transparency with AI features that are easily explained and robust auditing to ensure compliance. Data isolation, role-based accessibility, and secure integrations are all security measures. Emly's cost effective infrastructure allows for on-demand resource provisioning, policy management and risk reduction.

Enterprise AI applications can be built, deployed, and operated. C3 AI®, Suite uses a unique model driven architecture to speed delivery and reduce the complexity of developing enterprise AI apps. The C3 AI model-driven architecture allows developers to create enterprise AI applications using conceptual models, rather than long code. This has significant benefits: AI applications and models can be used to optimize processes for every product or customer across all regions and businesses. You will see results in just 1-2 quarters. Also, you can quickly roll out new applications and capabilities. You can unlock sustained value - hundreds to billions of dollars annually - through lower costs, higher revenue and higher margins. C3.ai's unified platform, which offers data lineage as well as governance, ensures enterprise-wide governance for AI.