Build AI Agents on Postmark
- No credit card required
- 5-minute setup
- SOC 2 Type II certified
Build AI Agents on top of Postmark
Build intelligent AI Agents on top of Postmark, with Peliqan’s AI data foundation:
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- Postmark MCP Server – let Claude, ChatGPT, or any AI client query and update your Postmark data
- Postmark in n8n AI Agents – build automated workflows that reason on your data
- Postmark in Make – trigger AI-powered actions from your Postmark events
- Text-to-SQL on Postmark – ask questions in plain English, get SQL-powered answers
- Postmark RAG – semantic search with out-of-the-box embeddings (vectors)
- Postmark Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine Postmark with 250+ other sources in one warehouse
Postmark MCP Server
Publish a Postmark MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Postmark data into its built-in warehouse – your AI agents query the cache, your live Postmark is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Postmark in plain English.
Postmark in n8n
Build AI Chatbots and AI Agents in n8n that query your Postmark data using Text-to-SQL and perform RAG on Postmark documents and records.
Peliqan acts as the data layer – syncing your Postmark data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on Postmark
Implement an AI Chatbot that answers analytical questions on Postmark 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 Postmark warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on Postmark
Implement RAG (retrieval-augmented generation) on Postmark data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Postmark 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 Postmark data with 250+ sources
Combine data from Postmark with 250+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Postmark, 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.
Prepare your Postmark data for AI
Access, combine, and report on data from Postmark 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 ?
Most AI tools can’t access your Postmark data directly. Peliqan solves this by syncing Postmark 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 Postmark 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 Postmark data in a single context.
How to build an AI agent for Postmark ?
There are different ways to build an AI agent that can query data in Postmark and take actions in Postmark. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Postmark 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 Postmark data, combined with data from 250+ other sources.
How can I use Postmark in n8n ?
First sign up for a free trial on Peliqan.io, next connect Postmark 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 Postmark data using Text to SQL.
How to use RAG with Postmark ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Postmark data. One option is to create a workflow in n8n that fetches all Postmark 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 Postmark ?
Connect Postmark in Peliqan, then create an API handler using the built-in MCP template. Expose the Postmark 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 Postmark is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for Postmark ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Postmark data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Postmark data in the data warehouse.
How to create embeddings in a vector store for Postmark data ?
In order to prepare your Postmark 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 Postmark data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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