Use a Scalable RAG Tool for Your Business
Use Latenode as a RAG tool and workflow platform for enterprise teams: connect data sources, structure content, run vector search, and give every agent accurate context before it answers a query.
RAG Tool for Business Data, Content, and Agent Context
Built for teams that need a free starting point and pro control to integrate internal documentation, external source data, and custom knowledge into AI agents, support applications, and retrieval-augmented generation workflows.
Connect Every Data Source to Your RAG Pipeline
Connect PDFs, docs, databases, APIs, websites, CRM records, and other data sources. Latenode turns each source into a reusable ingestion pipeline, so your agent can retrieve relevant information from the right dataset instead of guessing.
Index, Structure, and Embed Content for Accurate Answers
Prepare every input for retrieval: clean content, split it into a searchable structure, generate embeddings, and index it for semantic and BM25 matching. Combine vector search with keyword score logic to improve relevance and response quality.
Give Every AI Agent the Right Context From Your Data
Give each agent a governed knowledge base and a clear context layer. The agent receives only the most relevant data for the user query, then uses the selected model to produce relevant answers with better accuracy.
Use Any Model, Tool, or Integration in One RAG Engine
Build a compatible RAG engine around any model, native tool, or integration. Add custom logic, call an external API, route input through orchestration, and embed the result inside your application.
Scale From Free RAG Workflows to Pro and Enterprise Use Cases
Start free, prove the retrieval method, then scale the same RAG process into pro and enterprise deployments with built-in controls for documentation, data access, and agent governance.
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Free plan available · No credit card · First RAG workflow in minutes
What is a RAG tool?
A RAG tool connects a model to trusted data before it generates an answer. Latenode helps you build the retrieval pipeline, index content, and pass context to an agent inside a workflow.
Which data sources can Latenode use?
You can connect documentation, websites, files, databases, apps, APIs, and internal or external systems. Each source can become part of a custom knowledge base for your application.
How does retrieval improve accuracy?
The engine uses semantic search, vector search, BM25, filters, and ranking rules to find relevant information for a query. This gives the agent fresher data and improves the response.
Can we integrate this with existing tools and models?
Yes. You can integrate Latenode with your preferred model, native app actions, APIs, webhooks, and custom tools so the RAG pipeline works inside the business process you already run.
Is it compatible with open-source RAG stacks?
Yes. Latenode can work as an orchestration engine around open-source RAG components, hosted databases, embedding providers, and enterprise data controls.
Can teams start for free and scale later?
Yes. Start free, test a small dataset, then move to pro or enterprise patterns with built-in governance, access rules, monitoring, and repeatable agent deployment.
About Latenode
Latenode is an AI automation platform that helps teams build, connect, and deploy workflows across their entire business stack. From simple automations to production-ready AI agents, Latenode brings integrations, AI models, custom logic, and deployment controls into one visual workspace.
Overviews and webinars
Learn How to Build, Document, and Improve RAG Accuracy
Guides for teams building RAG pipelines, open-source RAG compatible architectures, agent tools, model selection, data source design, index logic, embeddings, vector search, and answer accuracy.
Guide
Retrieval-Augmented Generation (RAG): Complete AI Guide for 2025
Explore how Retrieval-Augmented Generation (RAG) enhances AI responses with real-time data retrieval, transforming industries like customer support and healthcare.
Deep DiveAgentic RAG: Complete Guide to Intelligent Retrieval-Augmented Generation
Explore how Agentic RAG systems revolutionize information retrieval through autonomous decision-making and dynamic workflows for enhanced accuracy.
GuideBest Embedding Models for RAG: Complete Guide to Free and Open Source Options
Explore the best free and open-source embedding models for Retrieval-Augmented Generation, balancing accuracy, speed, and cost for effective information retrieval.
TutorialRAG Diagram Guide: Visual Architecture of Retrieval-Augmented Generation
Explore how Retrieval-Augmented Generation (RAG) integrates real-time data retrieval with AI text generation for enhanced accuracy and relevance.
PlaybookRAG Chunking Strategies: Complete Guide to Document Splitting for Better Retrieval
Explore effective chunking strategies to enhance retrieval accuracy in RAG systems, balancing context and processing efficiency.