Build intelligent AI Agents on top of Power BI (private app) with Peliqan, the leading data foundation for the Agentic AI world.
Build intelligent AI Agents on top of Power BI (private app), with Peliqan’s AI data foundation:
Publish a Power BI (private app) MCP Server to query data from Power BI (private app) and to take actions in Power BI (private app) such as doing updates and adding new data in Power BI (private app).
Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Power BI (private app) data and perform RAG and Graph RAG on information from Power BI (private app).
Implement an AI Chatbot that can answer analytical data questions on Power BI (private app) data using “Text to SQL”.
Implement RAG (retrieval-augmented generation) on Power BI (private app) data with an out-of-the-box vector store (embeddings) of all business entities and other information in Power BI (private app).
Combine data from Power BI (private app) 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 Power BI (private app) 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 Power BI (private app) – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Power BI (private app) data for AI, including relational & non-relational data. Peliqan turns your Power BI (private app) 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 Power BI (private app) data. Use Peliqan to expose any Power BI (private app) 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 Power BI (private app) and take actions in Power BI (private app). For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Power BI (private app) 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 Power BI (private app) data, combined with data from 250+ other sources.
First sign up for a free trial on Peliqan.io, next connect Power BI (private app) 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 Power BI (private app) data using Text to SQL.
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Power BI (private app) data. One option is to create a workflow in n8n that fetches all Power BI (private app) 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 Power BI (private app) 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 Power BI (private app) data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Power BI (private app) data in the data warehouse.
In order to prepare your Power BI (private app) 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 Power BI (private app) data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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