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