Q7Leader

Build AI Agents on Q7Leader

Build intelligent AI Agents on top of Q7Leader with Peliqan, the leading data foundation for the Agentic AI world.

Let AI do the work for you

Build AI Agents on top of Q7Leader

Build intelligent AI Agents on top of Q7Leader, with Peliqan’s AI data foundation:

  • Q7Leader MCP Server
  • Q7Leader in n8n AI Agents
  • Q7Leader in Make
  • “Text to SQL” on Q7Leader data
  • Q7Leader RAG with out-of-the-box embeddings (vectors)
  • Q7Leader Graph RAG
  • Query unified 360° data combining Q7Leader and other data

Publish Data APIs

Q7Leader MCP Server

Publish a Q7Leader MCP Server to query data from Q7Leader and to take actions in Q7Leader such as doing updates and adding new data in Q7Leader.

Peliqan n8n AI Agents RAG and Text To SQL

Q7Leader in n8n

Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Q7Leader data and perform RAG and Graph RAG on information from Q7Leader.

Let AI do the work for you

Implement a “Text to SQL” chatbot on Q7Leader

Implement an AI Chatbot that can answer analytical data questions on Q7Leader data using “Text to SQL”. 

Spreadsheet BI for business users

Implement a chatbot with RAG on Q7Leader

Implement RAG (retrieval-augmented generation) on Q7Leader data with an out-of-the-box vector store (embeddings) of all business entities and other information in Q7Leader.

Combine Q7Leader data with 250+ other sources and build 360° views

Combine data from Q7Leader 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.

Prepare your Q7Leader data for AI

Access, combine, and report on data from Q7Leader and all your SaaS apps instantly.

Gain valuable insights by bringing all your business data together in one place within minutes.

Spreadsheet BI for business users

Security is our priority

SOC_2_Type_2

SOC 2 Type 2 validates our security controls, ensuring your data is protected by independently audited security measures.

iso2

ISO 27001 certification (in progress) ensures enterprise-grade information security, protecting your business with globally recognized standards.

GDPR-compliant

GDPR compliance guarantees EU data protection compliance, keeping PII data secure within EU boundaries.

Peliqan Overview

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Frequently asked questions

Peliqan is an all-in-one data platform with 250+ data connectors (ERP, CRM, Accounting, ATS/HRM, cloud storage etc.) – including Q7Leader – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Q7Leader data for AI, including relational & non-relational data. Peliqan turns your Q7Leader 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 Q7Leader data. Use Peliqan to expose any Q7Leader 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 Q7Leader and take actions in Q7Leader. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Q7Leader 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 Q7Leader data, combined with data from 250+ other sources.

First sign up for a free trial on Peliqan.io, next connect Q7Leader 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 Q7Leader data using Text to SQL.

There are different options to use RAG (retrieval augmented generation) in your AI Agent with Q7Leader data. One option is to create a workflow in n8n that fetches all Q7Leader 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 Q7Leader 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 Q7Leader data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Q7Leader data in the data warehouse.

In order to prepare your Q7Leader 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 Q7Leader data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.