Alternatives to Ragie

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.

Carbon is a cost-effective alternative to expensive pipelines. You only pay monthly for usage. Utilise less and spend less with our usage-based pricing; use more and save more. Use our ready-made components for file uploading, web scraping, and 3rd party verification. A rich library of APIs designed for developers that import AI-focused data. Create and retrieve chunks, embeddings and data from all sources. Unstructured data can be searched using enterprise-grade keyword and semantic search. Carbon manages OAuth flows from 10+ sources. It transforms source data to vector store-optimized files and handles data synchronization automatically.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

SweKit lets you build PR agents that review code, suggest improvement, enforce coding standard, identify potential issues and automate merge approvals. This streamlines the review process while improving code quality. Automate the creation of new features, debugging complex issues, creating and running tests, optimizing code for performance, maintainingability, and ensuring best practices across codebases, accelerating development. Use advanced code indexing and intelligent file navigation to explore and interact with huge codebases. Ask questions, track dependencies, uncover logic flow, and gain instant insight, enabling seamless communications with complex code structures. Keep your documentation up-to-date with your code. Update Mintlify documentation automatically whenever code changes are made. This ensures that your documentation is accurate, current, and ready for you and your team.

No one wants to have their AI respond to a client with outdated information. Neum AI provides accurate and current context for AI applications. Set up your data pipelines quickly by using built-in connectors. These include data sources such as Amazon S3 and Azure Blob Storage and vector stores such as Pinecone and Weaviate. Transform and embed your data using built-in connectors to embed models like OpenAI, Replicate and serverless functions such as Azure Functions and AWS Lambda. Use role-based controls to ensure that only the right people have access to specific vectors. Bring your own embedding model, vector stores, and sources. Ask us how you can run Neum AI on your own cloud.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

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.

Context Data is a data infrastructure for enterprises that accelerates the development of data pipelines to support Generative AI applications. The platform automates internal data processing and transform flows by using an easy to use connectivity framework. Developers and enterprises can connect to all their internal data sources and embed models and vector databases targets without the need for expensive infrastructure or engineers. The platform allows developers to schedule recurring flows of data for updated and refreshed data.

Graphlit simplifies the process of building an AI copilot or chatbot or adding LLMs to your existing application. Graphlit is a serverless platform that automates complex data processes, including data ingestion and extraction, LLM conversations. It also integrates webhooks, alerting, semantic search, and alerting. Graphlit's workflow as code approach allows you to programmatically define every step in the workflow. Data ingestion, metadata indexing, data preparation and data enrichment. Integration with your applications is achieved through event-based webhooks, API integrations, and integrations with event-based Webhooks.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

ChatGPT retrieval plug-in on steroids Intelligent document indexing services for smarter answers. For accurate ChatGPT responses, it is important to have text segments that respect the original document's context. OpenAI's text chunking services currently split the text only based on punctuation every 200 words. Pigro offers AI-based text chuncking services that divide content as a human would. They take into account the layout and structure of a document, including pagination, headings tables, lists, images etc. Our API supports Office-like documents in PDF, HTML and plain text. Pigro only delivers the relevant text to answer the query. Our generative AI expands your content by generating all possible questions within your document. Our search considers title, body and generated questions, as well as keywords and semantics. Generative indexing provides the best accuracy.

You can embed and store images, documents, and other information. No additional work required for high-performance retrieval workflows. Connect your data via the UI and API. Baseplate handles storage, embedding, and version control to ensure that your data is always up-to-date and in-sync. Hybrid Search with customized embeddings that are tailored to your data. No matter what type, size or domain of data you are searching for, you will get accurate results. Any LLM can be generated using data from your database. Connect search results to an App Builder prompt. It takes just a few clicks to deploy your app. Baseplate Endpoints allow you to collect logs, human feedback, etc. Baseplate Databases enable you to embed and store data in the same table with images, links, text, and other elements that make your LLM app great. You can edit your vectors via the UI or programmatically. We can version your data so that you don't have to worry about duplicates or stale data.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Redactive's platform for developers removes the specialist knowledge of data engineering that developers must learn, implement and maintain in order to build scalable, secure AI-enhanced apps for your customers, or productivity use cases for employees. Built with enterprise security in mind, so you can focus on quickly getting to production. Do not rebuild your permissions models just because your business is implementing AI. Redactive respects the access controls set by data sources & our data pipe can be configured so that your end documents are never stored, reducing your risks with downstream technology vendors. Redactive offers pre-built data connects & reusable authorization flows to connect with a growing list of tools. Custom connectors, LDAP/IdP integrations, and pre-built connectors are also available.

Simulations based on historical data can be used to improve models and update them incrementally. Data governance, access control, and versioning allow for easy collaboration. Ikigai has a wide range of integrations that make it easy to integrate with tools already in your workflow. Ikigai has 200+ connectors that allow you to connect to almost any data source. Want to push your ML to a dashboard or website? Integrate directly using Ikigai’s web integrations. Triggers can be used to run data synchronizations, and retrieve updates every time you run an automation flow. You can integrate Ikigai seamlessly by using your own APIs or creating APIs for your data stack.

Superpowered AI is a knowledge retrieval solution that's been designed specifically for LLM applications. We turn complex infrastructure into a few API calls. Give your LLMs private information, such as internal company documents, that was not in the training data. Store old messages in the Knowledge Base, and retrieve the most pertinent ones every time the user sends another message. Reduce hallucinations when you include relevant information in your prompts. Instruct the LLM only to use the information that it has been given. Superpowered AI's knowledge retrieval system allows you to retrieve the correct information and insert it directly into your LLM prompts. This allows you to provide highly relevant responses to users. Create a knowledge database directly from local files or folders, URLs, and query using REST API in less than 10 lines. Multi-stage knowledge retrieval pipeline that is state-of-the art to provide the most relevant results.

Observability allows you to detect data issues and ML problems faster, to deliver continuous improvements and to avoid costly incidents. Start with reliable data. Monitor data in motion for quality issues. Pinpoint data and models drift. Identify the training-serving skew, and proactively retrain. Monitor key performance metrics continuously to detect model accuracy degradation. Identify and prevent data leakage in generative AI applications. Protect your generative AI apps from malicious actions. Improve AI applications by using user feedback, monitoring and cross-team collaboration. Integrate in just minutes with agents that analyze raw data, without moving or replicating it. This ensures privacy and security. Use the proprietary privacy-preserving technology to integrate the WhyLabs SaaS Platform with any use case. Security approved by healthcare and banks.

Lilac is a free open-source tool that allows data and AI practitioners improve their products through better data. Understanding your data is easy with powerful filtering and search. Work together with your team to create a single dataset. Use best practices for data curation to reduce the size of your dataset and training costs and time. Our diff viewer allows you to see how your pipeline affects your data. Clustering is an automatic technique that assigns categories to documents by analyzing their text content. Similar documents are then placed in the same category. This reveals your dataset's overall structure. Lilac uses LLMs and state-of-the art algorithms to cluster the data and assign descriptive, informative titles. We can use keyword search before we do advanced searches, such as concept or semantic searching.

Unstructured data is the future for AI. This future is now possible. Build an ML/AI pipeline to scale all your unstructured data from one place. Graviti allows you to use better data to create better models. Learn about Graviti, the data platform that allows AI developers to manage, query and version control unstructured data. Quality data is no longer an expensive dream. All your metadata, annotations, and predictions can be managed in one place. You can customize filters and see the results of filtering to find the data that meets your needs. Use a Git-like system to manage data versions and collaborate. Role-based access control allows for safe and flexible team collaboration. Graviti's built in marketplace and workflow creator makes it easy to automate your data pipeline. No more grinding, you can quickly scale up to rapid model iterations.

Stop wasting your time managing integrations and fighting the limitations of "black-box" "solutions". Instantly integrate data from any source into your app in the format you prefer. 500+ API & DB Sources, connector SDKs, OAuth flows and flexible data models. Instant vector embeddings. Managed transactional & analytic storage. Instant SQL, REST & GraphQL APIs. Arch allows you to build AI-powered features based on your customer's data, without having to worry and maintain bespoke data infrastructure.

Semantic Kernel, a lightweight open-source development tool, allows you to easily build AI agents, and integrate the latest AI model into your C# or Python codebase. It is a middleware that allows rapid delivery of enterprise grade solutions. Semantic Kernel is flexible, modular and observable, which is why Microsoft and other Fortune 500 firms use it. With security-enhancing features like hooks, filters, and telemetry, you can be confident that you are delivering responsible AI at scale. It's reliable and committed to non-breaking changes. Version 1.0+ is supported across C# Python and Java. Existing chat-based APIs can be easily extended to support other modalities such as voice and video. Semantic Kernel is future-proof and connects your code with the latest AI models that evolve as technology advances.

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.

Choose the appropriate app type. Choose the output and input required for the app. Add data from existing sources such as files, URLs, sitemaps and YouTube links. You can also add data from Notion exports, Google Drive and other sources. Attach these data sources in the app builder. Save and publish your application. You can embed the code in your website or access it directly from the dedicated app page. Our APIs allow you to run the app directly from your application. Promptly offers embeddable widgets you can easily integrate in your website. These widgets can be used to create conversational AI applications, or to add a bot to your website. Customize the look and feel of the chatbot to match your website. Include a logo.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

Lyzr, a Generative AI enterprise company, offers private and secure AI Agents SDKs as well as an AI Management System. Lyzr helps businesses build, launch, and manage secure GenAI apps, whether they are on-prem or in the AWS cloud. No more sharing sensitive information with SaaS platforms, GenAI wrappers or GenAI platforms. Open-source tools are no longer prone to reliability and integration problems. Lyzr.ai is different from competitors like Cohere, Langchain and LlamaIndex. It follows a use case-focused approach. It builds full-service but highly customizable SDKs that simplify the addition of LLM functionality to enterprise applications. AI Agents Jazon - The AI SDR Skott is the AI digital marketer Kathy - the AI competitor analyst Diane - the AI HR manager Jeff - The AI Customer Success Manager Bryan - the AI inbound sales specialist Rachelz - the AI legal assistant

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.

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.

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.

Build, test and optimize Generative AI apps that unlock the value in your data. Our industry-leading ML expertise, our state-of-the art test and evaluation platform and advanced retrieval augmented-generation (RAG) pipelines will help you optimize LLM performance to meet your domain-specific needs. We provide an end-toend solution that manages the entire ML Lifecycle. We combine cutting-edge technology with operational excellence to help teams develop high-quality datasets, because better data leads better AI.

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.

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.

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.

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.

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.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

No more complicated templates and file restrictions. Integrate LLMs such as ChatGPT easily with vector databases, OCR PDF, and more. Chain prompts and transforms to create tailor-made AI experiences. From templates to adaptive chains. Our unique LLM features, such as quality control and semantic cache, can help you to save money and prevent hallucinations. We will take care of infrastructure management, hosting and scaling. Relevance AI will do the heavy lifting in just minutes. It can extract data from unstructured data in a flexible way. Relevance AI allows the team to extract data with over 90% accuracy within an hour.

Build your RAG system intuitively with fewer abstractions than solutions like LangChain. You can choose from a variety of hosted and remote providers, including vector databases, datasets and Large Language Models. SciPhi allows you to version control and deploy your system from anywhere using Git. SciPhi's platform is used to manage and deploy an embedded semantic search engine that has over 1 billion passages. The team at SciPhi can help you embed and index your initial dataset into a vector database. The vector database will be integrated into your SciPhi workspace along with your chosen LLM provider.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

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.

Your team can develop AI applications using models such as GPT-4, and operate them visually. You can deploy your application within 5 minutes, whether it is for internal team use or an external release. Using documents/webpages/Notion content as the context for AI, automatically complete text preprocessing, vectorization and segmentation. No need to learn embedding methods anymore. This will save you weeks of development. Dify offers a smooth user experience for model access and context embedding. It also provides cost control, data annotation, and cost control. You can easily create AI apps for internal team use, or product development. Start with a prompt but go beyond its limitations. Dify offers rich functionality in many scenarios.

The best data will help you achieve peak model performance. Create and manage training data for any visual modality. Debug models, boost performance and make foundation models yours. Expert review, QA, and QC workflows will help you deliver better datasets to your artificial-intelligence teams, improving model performance. Encord's Python SDK allows you to connect your data and models, and create pipelines that automate the training of ML models. Improve model accuracy by identifying biases and errors in your data, labels, and models.

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.

Katonic Generative AI Platform allows you to build powerful enterprise-grade AI applications in minutes without any coding. Generative AI can boost your employees' productivity and improve your customer service. Create AI-powered digital assistants and chatbots that can access, process and refresh information from documents and dynamic content automatically using pre-built connectors. You can extract information from unstructured texts or uncover insights in specialized domains without creating templates. Transform dense text, such as financial reports, meeting transcripts, etc., into a personalized executive summary, capturing key information. Build recommendation systems to suggest products, content, or services based on past behavior and preferences.

Optimizing continuous pre-training to enrich models with proprietary data. Assuring domain-specific models provide a smooth user experience. Create a production-friendly RAG pipeline that offers ongoing support. With Arcee's SLM Adaptation system, you do not have to worry about fine-tuning, infrastructure set-up, and all the other complexities involved in stitching together solutions using a plethora of not-built-for-purpose tools. Our product's domain adaptability allows you to train and deploy SLMs for a variety of use cases. Arcee's VPC service allows you to train and deploy your SLMs while ensuring that what belongs to you, stays yours.

Aigur makes it easy to create multi-user generative AI applications that manage data pipelines, user plans, security, and other aspects. We can help you manage and monitor your users' usage plans. You can grant and regenerate credits, limit usage over the time, use partial credits, and much more. A visual editor allows you to define your backend logic. It is supported by code that can be edited. Our pre-defined pipelines will reduce development time so you can focus on the logic of your application. Aigur makes it simple to develop your front end. Aigur's prebuilt component library for web and mobile AI allows you to authenticate users and handle job updates based upon web sockets. This will cut down development time and help you reduce your development time. You will receive a simple API that allows you to manage the user flows. You don't have to replace your entire infrastructure. Just call a few functions, and you're done. For more information, see our opensource library's documentation.

At competitive prices, you can access dedicated machines that are pre-configured with professional AI development tools. Apolo offers everything from HPC resources to a complete AI platform with a built-in ML toolkit. Apolo is available in a distributed architecture or as a dedicated enterprise cloud. It can also be deployed as a white-label multi-tenant solution that supports dedicated instances or self service cloud. Apolo creates a fully-fledged AI development environment, with all the tools needed at your fingertips. Apolo automates and manages the infrastructure for successful AI development. Apolo's AI services seamlessly integrate your on-prem resources and cloud resources. They also deploy pipelines and integrate your commercial and open-source development tools. Apolo provides enterprises with the resources and tools necessary to achieve breakthroughs when it comes to AI.

Connecting every data source with analytics services on a single AI-powered platform will transform how people access, manage, and act on data and insights. All your data. All your teams. All your teams in one place. Create an open, lake-centric hub to help data engineers connect data from various sources and curate it. This will eliminate sprawl and create custom views for all. Accelerate analysis through the development of AI models without moving data. This reduces the time needed by data scientists to deliver value. Microsoft Teams, Microsoft Excel, and Microsoft Teams are all great tools to help your team innovate faster. Connect people and data responsibly with an open, scalable solution. This solution gives data stewards more control, thanks to its built-in security, compliance, and governance.

IBM Watson OpenScale provides visibility into the creation and use of AI-powered applications in an enterprise-scale environment. It also allows businesses to see how ROI is delivered. IBM Watson OpenScale provides visibility to companies about how AI is created, used, and how ROI is delivered at business level. You can create and deploy trusted AI using the IDE you prefer, and provide data insights to your business and support team about how AI affects business results. Capture payload data, deployment output, and alerts to monitor the health of business applications. You can also access an open data warehouse for custom reporting and access to operations dashboards. Based on business-determined fairness attributes, automatically detects when artificial Intelligence systems produce incorrect results at runtime. Smart recommendations of new data to improve model training can reduce bias.

Write down the superpowers your AI app has. You can ask it to do almost anything, from mining data to writing lyrics. Give your app a fresh look. Change the colors, fonts, themes and domains. to personalize it. Publish your own ChatGPt app in public, and share the link with users. Sttabot can turn your ideas into an AI application that is live. Define your platform's capabilities and edit the interface in order to create your custom app. Integrate your app with multiple platforms. Use embeddings for your Sttabot application to connect with your landing page, and turn it into an explosive startup.

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.