Build AI Agents on Weclapp
Build intelligent AI Agents on top of Weclapp with Peliqan, the leading data foundation for the Agentic AI world.
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- 5-minute setup
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Build AI Agents on top of Weclapp
Build intelligent AI Agents on top of Weclapp, with Peliqan’s AI data foundation:
- Weclapp MCP Server
- Weclapp in n8n AI Agents
- Weclapp in Make
- “Text to SQL” on Weclapp data
- Weclapp RAG with out-of-the-box embeddings (vectors)
- Weclapp Graph RAG
- Query unified 360° data combining Weclapp and other data
Weclapp MCP Server
Publish a Weclapp MCP Server to query data from Weclapp and to take actions in Weclapp such as doing updates and adding new data in Weclapp.
Weclapp in n8n
Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Weclapp data and perform RAG and Graph RAG on information from Weclapp.
Implement a “Text to SQL” chatbot on Weclapp
Implement an AI Chatbot that can answer analytical data questions on Weclapp data using “Text to SQL”.
Implement a chatbot with RAG on Weclapp
Implement RAG (retrieval-augmented generation) on Weclapp data with an out-of-the-box vector store (embeddings) of all business entities and other information in Weclapp.
Combine Weclapp data with 250+ sources
Combine data from Weclapp 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 Weclapp data for AI
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.
Unify, Automate & Activate Your Data
Connect all your SaaS apps, databases, and spreadsheets into one workspace. Build automations, analytics pipelines, and data apps — all in one place.
Frequently Asked Questions
Why do I need Peliqan for my AI ?
Peliqan is an all-in-one data platform with 250+ data connectors (ERP, CRM, Accounting, ATS/HRM, cloud storage etc.) – including Weclapp – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Weclapp data for AI, including relational & non-relational data. Peliqan turns your Weclapp 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 Weclapp data. Use Peliqan to expose any Weclapp as an MCP server to query data and to take actions.
How to build an AI agent for Weclapp ?
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.
How can I use Weclapp in n8n ?
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.
How to use RAG with Weclapp ?
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.
How to build an MCP Server for Weclapp ?
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 Weclapp such as querying data, doing lookups, adding new items or performing updates.
How do I build an AI chatbot with “Text to SQL” for Weclapp ?
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.
How to create embeddings in a vector store for Weclapp data ?
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.
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