Build intelligent AI Agents on top of Azure blob storage with Peliqan, the leading data foundation for the Agentic AI world.
Build intelligent AI Agents on top of Azure blob storage, with Peliqan’s AI data foundation:
Publish a Azure blob storage MCP Server to query data from Azure blob storage and to take actions in Azure blob storage such as doing updates and adding new data in Azure blob storage.
Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Azure blob storage data and perform RAG and Graph RAG on information from Azure blob storage.
Implement an AI Chatbot that can answer analytical data questions on Azure blob storage data using “Text to SQL”.
Implement RAG (retrieval-augmented generation) on Azure blob storage data with an out-of-the-box vector store (embeddings) of all business entities and other information in Azure blob storage.
Combine data from Azure blob storage with data from 250+ other connectors, and build 360° views of business entities such as customers, leads, products, employees etc.
Feed unified 360° data models to your AI Agents with RAG and “Text to SQL”. Allow your AI Agents to access all business data in one uniform data model.
Access, combine, and report on data from Azure blob storage and all your SaaS apps instantly. Gain valuable insights by bringing all your business data together in one place within minutes.
Connect all your SaaS apps, databases, and spreadsheets into one workspace. Build automations, analytics pipelines, and data apps — all in one place.
Peliqan is an all-in-one data platform with 250+ data connectors (ERP, CRM, Accounting, ATS/HRM, cloud storage etc.) – including Azure blob storage – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Azure blob storage data for AI, including relational & non-relational data. Peliqan turns your Azure blob storage data into 360° views that can be used in AI Agents built in n8n, Make, langChain, langGraph or any other framework. Use Peliqan to create embeddings, store them in a vector store so that your AI chatbots can use RAG and Graph RAG, combined with Text-to-SQL for analytical reasoning. Peliqan is the only platform that allows your AI Agents to combine RAG and Text-to-SQL to apply deep reasoning on your Azure blob storage data. Use Peliqan to expose any Azure blob storage as an MCP server to query data and to take actions.
There are different ways to build an AI agent that can query data in Azure blob storage and take actions in Azure blob storage. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Azure blob storage data to its built-in data warehouse and allow the AI Agent to perform “Text to SQL” and RAG to answer questions and to perform reasoning on Azure blob storage data, combined with data from 250+ other sources.
First sign up for a free trial on Peliqan.io, next connect Azure blob storage in Peliqan. Once that is done, create an AI agent in n8n and use the Peliqan n8n node in your worflow. Add Peliqan as a “tool” to your AI Agent node, so that the AI agent can query your Azure blob storage data using Text to SQL.
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Azure blob storage data. One option is to create a workflow in n8n that fetches all Azure blob storage data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
In Peliqan, you can set up API endpoints and expose them as MCP Server. In the API endpoint handler script, you can configure actions to be taken in Azure blob storage such as querying data, doing lookups, adding new items or performing updates.
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Azure blob storage data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Azure blob storage data in the data warehouse.
In order to prepare your Azure blob storage data for RAG, you need to create embeddings and store them in a vector store. This can be done by creating a workflow in n8n that fetches all Azure blob storage data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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