Peliqan

Build AI Agents on Kadaster

Build intelligent AI Agents on top of Kadaster with Peliqan. Query your data in plain English, automate workflows with AI, and write changes back – all through a single platform with 250+ connectors and a built-in data warehouse.

Kadaster

Build AI Agents

Build AI Agents on top of Kadaster

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

    • Kadaster MCP Server – let Claude, ChatGPT, or any AI client query and update your Kadaster data
    • Kadaster in n8n AI Agents – build automated workflows that reason on your data
    • Kadaster in Make – trigger AI-powered actions from your Kadaster events
    • Text-to-SQL on Kadaster – ask questions in plain English, get SQL-powered answers
    • Kadaster RAG – semantic search with out-of-the-box embeddings (vectors)
    • Kadaster Graph RAG – traverse relationships across your data entities
    • Unified 360° data – combine Kadaster with 250+ other sources in one warehouse

Kadaster MCP Server

Publish a Kadaster MCP Server with full read and write capabilities.

Peliqan syncs a read-only copy of your Kadaster data into its built-in warehouse – your AI agents query the cache, your live Kadaster is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Kadaster in plain English.

Build MCP Server

Build AI agents in n8n

Kadaster in n8n

Build AI Chatbots and AI Agents in n8n that query your Kadaster data using Text-to-SQL and perform RAG on Kadaster documents and records.

Peliqan acts as the data layer – syncing your Kadaster data, converting natural language to SQL, and returning results your n8n agent can act on.

Implement a “Text to SQL” chatbot on Kadaster

Implement an AI Chatbot that answers analytical questions on Kadaster 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 Kadaster warehouse, and returns structured results – no SQL knowledge required.

Text to SQL

Implement a chatbot

Implement a chatbot with RAG on Kadaster

Implement RAG (retrieval-augmented generation) on Kadaster data with Peliqan’s out-of-the-box vector store.

Peliqan automatically creates embeddings of your Kadaster 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 Kadaster data with 250+ sources

Combine data from Kadaster with 250+ other connectors in Peliqan’s built-in warehouse.

Build unified 360° views of customers, deals, products, or employees – pulling from Kadaster, 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.

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Combine data

Prepare your Kadaster data for AI

Access, combine, and report on data from Kadaster and all your SaaS apps instantly. Gain valuable insights by bringing all your business data together in one place within minutes.

SaaS Data Cockpit

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.

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Frequently Asked Questions

Why do I need Peliqan for my AI ?

Most AI tools can’t access your Kadaster data directly. Peliqan solves this by syncing Kadaster 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 Kadaster 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 Kadaster data in a single context.

There are different ways to build an AI agent that can query data in Kadaster and take actions in Kadaster. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Kadaster 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 Kadaster data, combined with data from 250+ other sources.

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

There are different options to use RAG (retrieval augmented generation) in your AI Agent with Kadaster data. One option is to create a workflow in n8n that fetches all Kadaster data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.

Connect Kadaster in Peliqan, then create an API handler using the built-in MCP template. Expose the Kadaster 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 Kadaster 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 Kadaster data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Kadaster data in the data warehouse.

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

Peliqan data platform

All-in-one Data Platform

Built-in data warehouse, superior data activation capabilities, and AI-powered development assistance.

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