Top 50 Retrieval Augmented Generation

Provides a fully managed Retrieval-Augmented Generation (RAG) service that enables developers to integrate and process structured and unstructured data from sources like Google Drive, Notion, and Confluence using APIs and SDKs. Automates data ingestion, chunking, indexing, and retrieval with features like LLM re-ranking, hybrid search, and entity extraction, reducing development time from months to weeks while ensuring accurate, context-rich AI outputs.

SoftlyAI provides Retrieval-Augmented Generation solutions that enable knowledge workers to efficiently access and utilize relevant data through context-aware AI associates. The platform enhances productivity by streamlining workflows for healthcare and finance professionals, allowing for personalized interactions and improved decision-making.

Saldor offers a retrieval-augmented generation platform that integrates with existing tech stacks to extract and utilize information from external knowledge bases. This technology enhances data accessibility and improves decision-making processes for businesses by providing timely and relevant insights.

Voyage AI develops embedding models and rerankers that enhance search accuracy and efficiency in retrieval-augmented generation (RAG) applications. Their technology improves the relevance of search results, enabling users to access precise information quickly and effectively.

SciPhi offers an open-source platform, R2R, that enables developers to build, test, and deploy Retrieval-Augmented Generation (RAG) systems with features like document ingestion, hybrid vector search, and user authentication. This solution addresses the complexity of infrastructure management, allowing developers to focus on creating AI applications that deliver instant, AI-powered responses.

Vectara offers a generative AI platform that integrates retrieval-augmented generation capabilities into enterprise applications, enabling the development of AI agents and assistants that can analyze data and execute action plans. The platform minimizes hallucinations through advanced machine learning models and ensures data security with SOC-2 compliance, addressing the need for reliable and efficient AI solutions in mission-critical environments.

AI21 Labs develops generative AI systems that utilize advanced foundation models and a built-in Retrieval-Augmented Generation (RAG) engine to create conversational AI applications grounded in enterprise data. Their technology enhances enterprise workflows by providing accurate, reliable, and scalable AI solutions tailored to specific organizational needs.

Provides a secure platform for enterprises to build and deploy Retrieval-Augmented Generation (RAG) applications that integrate with existing data sources while enforcing access controls and compliance. It prevents data leakage and protects sensitive information through real-time permission synchronization, automatic PII redaction, and comprehensive audit logging. The flexible API and low-code tools enable seamless deployment of AI-powered workflows, such as secure chatbots and enterprise search, across various platforms.

Contextual AI provides a unified context engineering platform that accelerates the development of production-grade AI agents by abstracting the Retrieval-Augmented Generation (RAG) workflow. Its RAG 2.0 models and Component APIs deliver higher accuracy and scalability for enterprises needing to extract insights from unstructured documents while ensuring data security and compliance.

Statespace offers a markdown‑first framework that lets engineers define retrieval‑augmented generation pipelines, data connectors, language model endpoints, and tool integrations directly in .md files, which are parsed at runtime to generate the necessary execution logic. A single CLI command launches a local HTTP endpoint with auto‑generated Swagger UI for testing, and the system supports containerized production deployments with API‑key usage controls. The solution targets AI/ML engineers and enterprises seeking rapid prototyping and low‑ops RAG applications, providing an open‑source core with optional paid usage plans.

Tavily Search API is a specialized search engine designed for Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), providing real-time access to accurate and relevant web data. It addresses the challenge of information retrieval by delivering concise, verifiable results that enhance the performance of AI applications while minimizing data hallucinations.

Algolia offers a cloud‑native AI Retrieval Platform that unifies search, recommendation, and generative AI into a single service. It provides sub‑100 ms neural‑ranked results, real‑time behavioral suggestions, and retrieval‑augmented generation through REST APIs and pre‑built SDKs for web, mobile, and voice interfaces. The platform also includes data enrichment at index time, custom AI agents, and enterprise‑grade security across a global multi‑region infrastructure.

Vectorize is a cloud service that transforms unstructured data into optimized vector search indexes for retrieval-augmented generation (RAG) applications. It automates the extraction, evaluation, and deployment of AI-ready vectors from various knowledge repositories, ensuring real-time updates for accurate search results.

Epsilla is an all-in-one platform that enables the rapid development and deployment of production-ready AI agents using private data and knowledge, leveraging vertical large language models (LLMs) and advanced retrieval-augmented generation (RAG) techniques. The platform addresses inefficiencies in data management and application development, allowing users to create AI solutions up to ten times faster while significantly reducing operational costs.

Langbase is a serverless AI developer platform that enables developers to create, deploy, and manage AI agents using composable memory and retrieval-augmented generation (RAG) techniques. This platform addresses the complexity of AI infrastructure by providing a unified API for over 100 large language models, allowing for rapid iteration and significant cost savings in AI development.

App Orchid provides an enterprise AI platform that uses a patented knowledge‑graph semantic layer to automatically connect and enrich structured and unstructured data sources into a unified, LLM‑ready fabric. The platform includes a Semantic SQL engine for federated, explainable queries and a conversational interface that delivers accurate retrieval‑augmented generation and auto‑visualizations while enforcing role‑based security and compliance. It integrates via REST/GraphQL APIs and can be deployed on cloud or on‑premises.

ProductNow provides AI-powered assistants that integrate with existing product and program management tools to automate roadmap creation, feature prioritization, and launch coordination. The platform uses large language models and retrieval‑augmented generation to deliver context‑aware recommendations and synchronize tasks across roles in real time, improving decision speed and alignment for enterprise product teams.

DataStax provides an AI‑ready platform that unifies a hyper‑converged NoSQL database (Astra DB) with a low‑code workflow builder (Langflow) and native integration to IBM watsonx. The solution automates real‑time ingestion, enrichment, and vector/knowledge‑graph search of unstructured data while delivering enterprise security, role‑based access, and multi‑cloud or on‑prem deployment options. It enables enterprise data and AI teams to build and operate retrieval‑augmented generation and multi‑agent AI applications with reduced operational overhead.

Pryon provides an AI-driven RAG Suite that combines advanced ingestion and retrieval engines with generative large language models to deliver accurate and verifiable answers at enterprise scale. This technology addresses knowledge friction by enabling organizations to access and utilize their internal information efficiently, enhancing decision-making and productivity.

Nuclia provides a RAG-as-a-Service platform that automatically indexes unstructured data from various sources, enabling developers to implement AI search and generative answers tailored to specific use cases. This technology enhances data accessibility and insight generation while ensuring data governance and reducing the costs associated with building custom AI solutions.

Corvic provides a multi-spatial Embedding Ops platform that generates and manages high-quality embeddings from complex data types, including tables and graphs, using GraphAI and GenerativeAI technologies. This platform enhances data analysis accuracy and enables actionable insights, addressing the limitations of traditional retrieval-augmented generation (RAG) methods.

Neum AI provides an open-source framework for building scalable Retrieval-Augmented Generation (RAG) pipelines, enabling developers to efficiently manage data flows and real-time synchronization with vector databases. This technology addresses the challenge of integrating and embedding large-scale data into AI applications, ensuring high performance and reliability.

Unleash provides an enterprise AI platform that ingests data from SaaS tools via over 100 connectors and creates a retrieval‑augmented knowledge index. The platform offers no‑code AI assistants and semantic search that can be embedded in Slack, Salesforce, Teams, and other workflow applications, with LLM‑agnostic support and deployment options from shared SaaS to self‑hosted, all under enterprise security and compliance controls.

Strative is an enterprise Retrieval-Augmented Generation (RAG) platform that enhances generative AI accuracy and compliance for organizations in regulated sectors like finance and healthcare. By utilizing advanced semantic search and hybrid queries, Strative enables users to achieve 10-15% improvements in RAG accuracy while ensuring responsible AI deployment.

Denser provides an AI platform utilizing Retrieval-Augmented Generation (RAG) to create chat interfaces over organizational data like PDFs, websites, and databases. It delivers answers anchored to exact source passages, offering transparency through source highlighting and enabling automated actions via SQL queries and API integrations. The platform scales for internal knowledge assistants or customer-facing chatbots, accessible via a no-code widget or REST API.

AR-Generation develops augmented reality applications that enhance user experiences across various sectors, including education, entertainment, and travel. Their technology provides an accessible platform for users to create and interact with personalized AR content, addressing the need for affordable and practical AR solutions in everyday life.

The startup develops agentic graph retrieval methods that enhance retrieval pipelines through self-improving vector databases and knowledge graphs. These technologies enable users to create sophisticated retrieval-augmented generation (RAG) pipelines in plain English, improving performance over traditional methods while managing dynamic data.

Ayfie Group provides RAG (Retrieval Augmented Generation) powered enterprise search and text analytics solutions that enhance data retrieval from diverse sources while maintaining document hierarchy for contextually accurate insights. Their technology optimizes workflows by delivering real-time, relevant information, enabling data-driven decision-making without the need for extensive system restructuring.

LayerNext is a no-code platform that utilizes large language models and retrieval-augmented generation to automate data analysis and generate actionable business insights. It reduces ad hoc analysis time by five times and increases data team productivity by 75%, enabling users to independently uncover insights through natural language queries.

Caden AI provides a platform for building AI applications using Retrieval-Augmented Generation (RAG) and GraphRAG, allowing users to transform unstructured data into structured formats and deploy knowledge graphs with any language model. The service eliminates the need for infrastructure management by offering fully hosted solutions, enabling rapid integration and experimentation with various data sources and APIs.

Parasail provides scalable, high-performance AI compute for open-source models, enabling enterprises to deploy and optimize workloads like retrieval-augmented generation and multimodal processing. The platform reduces costs and complexity by offering serverless APIs, dedicated hardware, and automated tuning, achieving up to 10x cost savings while ensuring efficient batch and real-time processing.

Promptev provides a context‑first AI platform that centralizes prompt management, version control, and data connectivity for Retrieval‑Augmented Generation applications. It automatically creates embeddings, chunked data, and knowledge graphs from connected sources such as Google Drive, Notion, and SharePoint, and routes queries through vector and graph search without custom pipeline code. The service includes JavaScript and Python SDKs, a REST API, a no‑code editor, and enterprise security features for product, engineering, and operational teams.

Provides a developer-friendly API for building production-ready AI agents and features without requiring AI expertise. The platform automates the creation of optimized AI pipelines by chaining retrieval-augmented generation (RAG) components, enabling seamless data ingestion, query translation, and model deployment across various programming languages and data sources.

Elqano utilizes AI-driven semantic search and retrieval-augmented generation to automatically tag and organize an organization’s key documents, making them easily accessible and shareable. This technology addresses the challenge of inefficient knowledge management by enhancing information retrieval and streamlining employee workflows.

Verdea provides a digital compliance platform that transforms ESRS requirements into machine-readable formats, utilizing a proprietary sustainability database and a Retrieval-Augmented Generation model for precise data indexing. This technology enables companies to efficiently generate accurate CSRD reports while ensuring compliance with evolving regulations.

Retrieva develops machine learning-based software solutions that support businesses in executing AI projects, including the implementation of Retrieval-Augmented Generation (RAG) systems and the construction of embedding models. The company addresses the challenges organizations face in effectively utilizing AI technologies by providing tailored technical expertise and comprehensive project support.

Moterra offers a private‑cloud generative AI platform that runs within a customer’s own cloud environment, enabling retrieval‑augmented generation over internal repositories such as SharePoint, Google Drive, and relational databases. The solution provides task‑specific assistants for knowledge search, content drafting, data analysis, and document comparison, all with role‑based access, audit logs, and compliance certifications (ISO 27001, GDPR, SOC 2).

DropChat provides AI chatbots that utilize GPT-4 and Retrieval Augmented Generation (RAG) to deliver accurate, context-specific responses based on user-provided data sources. The platform enables businesses to automate customer service interactions, reducing response times and improving user satisfaction while allowing for seamless escalation to human agents when needed.

Climind is a platform that utilizes Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to provide precise climate risk assessments and generate ESG climate disclosure reports. By integrating corporate climate data with mitigation strategies, Climind enhances decision-making for industries transitioning to low-carbon operations.

Provides custom generative AI solutions, including fine-tuned large language models, retrieval-augmented generation systems, and state-of-the-art image generation using techniques like Stable Diffusion. Enables startups and enterprises to rapidly develop and deploy AI-powered products, such as chatbots, MVPs, and on-premises systems, while improving efficiency, accuracy, and scalability through tailored data pipelines and model evaluation.

EyeLevel provides a platform for building Retrieval-Augmented Generation (RAG) applications that utilize enterprise data to deliver accurate and secure AI solutions. By enabling companies to ingest, store, and search complex documents, EyeLevel addresses the challenge of generating reliable outputs from large language models, achieving up to 95% accuracy in various applications across industries.

Elotl provides a serverless infrastructure platform designed for deploying and managing microservices, specifically tailored for AI applications. The platform enables organizations to self-host large language models, retrieval-augmented generation, and vector databases, mitigating the high costs and data privacy risks associated with public GenAI inference APIs.

QuePasa provides a Retrieval-Augmented Generation (RAG) API that enhances data retrieval accuracy for specialized datasets, achieving twice the precision of competitors like Langchain. This solution enables businesses to efficiently integrate and analyze their unique data, ensuring reliable insights for critical applications such as financial analysis.

Data Canon provides a custom AI Research Terminal that integrates internal knowledge bases and external data sources with large language models (LLMs) using retrieval-augmented generation (RAG) techniques. This solution enables macro and geopolitical advisory firms to access real-time, relevant insights while maintaining data privacy and reducing the need for costly model training.

Datapher AI provides an agentic investment analyst that utilizes contextualized retrieval-augmented generation and a proprietary self-learning knowledge graph to automate research and analytical tasks in portfolio management. This technology reduces the time investment professionals spend on manual tasks by 80%, enabling them to concentrate on alpha-generating strategies.

Provides a SaaS platform that leverages AI-powered chatbots and retrieval-augmented generation (RAG) to deliver contextually relevant responses in real time. This solution improves customer support efficiency and accuracy by integrating advanced natural language processing with dynamic information retrieval, enabling businesses to handle inquiries faster and reduce operational costs.

Provides an enterprise-grade platform for continuous optimization of machine learning systems, focusing on hyperparameter tuning, custom evaluation metrics, and real-time performance maintenance. It addresses challenges in AI deployment by ensuring models, such as retrieval-augmented generation and LLMs, remain efficient, secure, and accurate in production through systematic experimentation and automated parameter adjustments.

RainMakerz utilizes AI-driven technology, including domain-specific large language models and Retrieval-Augmented Generation, to automate the creation of interactive pitch decks and provide real-time investor Q&A. This platform enhances fundraising efficiency by delivering tailored insights and improving investor relations through dynamic analytics and secure collaboration tools.

Dataworkz provides a platform for businesses to build and deploy Generative AI applications using Retrieval Augmented Generation (RAG) without the need for infrastructure management or advanced developer skills. The solution enables rapid data ingestion, transformation, and optimization, allowing teams to enhance customer experiences and improve productivity through tailored AI applications.

Vectify AI provides Mafin, a financial AI model that utilizes Retrieval Augmented Generation to deliver accurate, hallucination-free financial insights and real-time data access. Mafin enhances financial research efficiency by integrating up-to-date SEC filings, earnings calls, and customizable financial metrics calculations.