Build AI Agents on TraxGo
- No credit card required
- 5-minute setup
- SOC 2 Type II certified
Build AI Agents on top of TraxGo
Build intelligent AI Agents on top of TraxGo, with Peliqan’s AI data foundation:
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- TraxGo MCP Server – let Claude, ChatGPT, or any AI client query and update your TraxGo data
- TraxGo in n8n AI Agents – build automated workflows that reason on your data
- TraxGo in Make – trigger AI-powered actions from your TraxGo events
- Text-to-SQL on TraxGo – ask questions in plain English, get SQL-powered answers
- TraxGo RAG – semantic search with out-of-the-box embeddings (vectors)
- TraxGo Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine TraxGo with 250+ other sources in one warehouse
TraxGo MCP Server
Publish a TraxGo MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your TraxGo data into its built-in warehouse – your AI agents query the cache, your live TraxGo is never touched. Connect Claude, ChatGPT, or any MCP client and start querying TraxGo in plain English.
TraxGo in n8n
Build AI Chatbots and AI Agents in n8n that query your TraxGo data using Text-to-SQL and perform RAG on TraxGo documents and records.
Peliqan acts as the data layer – syncing your TraxGo data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on TraxGo
Implement an AI Chatbot that answers analytical questions on TraxGo 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 TraxGo warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on TraxGo
Implement RAG (retrieval-augmented generation) on TraxGo data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your TraxGo 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 TraxGo data with 250+ sources
Combine data from TraxGo with 250+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from TraxGo, 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 TraxGo data for AI
Access, combine, and report on data from TraxGo 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 TraxGo data directly. Peliqan solves this by syncing TraxGo 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 TraxGo 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 TraxGo data in a single context.
How to build an AI agent for TraxGo ?
There are different ways to build an AI agent that can query data in TraxGo and take actions in TraxGo. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your TraxGo 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 TraxGo data, combined with data from 250+ other sources.
How can I use TraxGo in n8n ?
First sign up for a free trial on Peliqan.io, next connect TraxGo 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 TraxGo data using Text to SQL.
How to use RAG with TraxGo ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with TraxGo data. One option is to create a workflow in n8n that fetches all TraxGo 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 TraxGo ?
Connect TraxGo in Peliqan, then create an API handler using the built-in MCP template. Expose the TraxGo 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 TraxGo is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for TraxGo ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your TraxGo data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the TraxGo data in the data warehouse.
How to create embeddings in a vector store for TraxGo data ?
In order to prepare your TraxGo 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 TraxGo data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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