Build intelligent AI Agents on top of Weclapp, with Peliqan’s AI data foundation:
Publish a Weclapp MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Weclapp data into its built-in warehouse – your AI agents query the cache, your live Weclapp is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Weclapp in plain English.
Build AI Chatbots and AI Agents in n8n that query your Weclapp data using Text-to-SQL and perform RAG on Weclapp documents and records.
Peliqan acts as the data layer – syncing your Weclapp data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement an AI Chatbot that answers analytical questions on Weclapp data using Text-to-SQL.
Ask “what were my top 10 customers last quarter?” or “which deals are stalled?” and get instant, accurate answers.
Peliqan converts your question to SQL, runs it against your Weclapp warehouse, and returns structured results – no SQL knowledge required.
Implement RAG (retrieval-augmented generation) on Weclapp data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Weclapp records, notes, and documents – so your AI agents can search semantically, not just by keywords.
Combine RAG with Text-to-SQL for AI that can both reason on numbers and understand context.
Combine data from Weclapp with 250+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Weclapp, your CRM, ERP, HR systems, and more.
Feed these unified data models to your AI Agents so they can answer cross-functional questions in a single query.
Access, combine, and report on data from Weclapp 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.
Most AI tools can’t access your Weclapp data directly. Peliqan solves this by syncing Weclapp into a built-in data warehouse and exposing it through MCP, Text-to-SQL, and RAG.
Your AI agents get governed, real-time access to Weclapp data combined with 250+ other sources – without building custom integrations.
Peliqan is the only platform that lets AI agents combine structured SQL queries with semantic RAG search on Weclapp data in a single context.
There are different ways to build an AI agent that can query data in Weclapp and take actions in Weclapp. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Weclapp 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 Weclapp data, combined with data from 250+ other sources.
First sign up for a free trial on Peliqan.io, next connect Weclapp 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 Weclapp data using Text to SQL.
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Weclapp data. One option is to create a workflow in n8n that fetches all Weclapp data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
Connect Weclapp in Peliqan, then create an API handler using the built-in MCP template. Expose the Weclapp tables and actions your AI needs, set role-based permissions, and run pip install mcp-server-peliqan to connect Claude or ChatGPT. Your AI agents query a cached copy of your data – your live Weclapp is never touched. The full setup takes under 10 minutes.
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Weclapp data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Weclapp data in the data warehouse.
In order to prepare your Weclapp 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 Weclapp data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
Built-in data warehouse, superior data activation capabilities, and AI-powered development assistance.
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