Build AI Agents on Harvest
Build intelligent AI Agents on top of Harvest with Peliqan. Query your data in plain English, automate workflows with AI, and write changes back – all through a single platform with 300+ connectors and a built-in data warehouse.
- No credit card required
- 5-minute setup
- SOC 2 Type II certified
Build AI Agents on top of Harvest
Build intelligent AI Agents on top of Harvest, with Peliqan’s AI data foundation:
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- Harvest MCP Server – let Claude, ChatGPT, or any AI client query and update your Harvest data
- Harvest in n8n AI Agents – build automated workflows that reason on your data
- Harvest in Make – trigger AI-powered actions from your Harvest events
- Text-to-SQL on Harvest – ask questions in plain English, get SQL-powered answers
- Harvest RAG – semantic search with out-of-the-box embeddings (vectors)
- Harvest Graph RAG – traverse relationships across your data entities
- Unified 360° data – combine Harvest with 250+ other sources in one warehouse
Harvest MCP Server
Publish a Harvest MCP Server with full read and write capabilities.
Peliqan syncs a read-only copy of your Harvest data into its built-in warehouse – your AI agents query the cache, your live Harvest is never touched. Connect Claude, ChatGPT, or any MCP client and start querying Harvest in plain English.
Harvest in n8n
Build AI Chatbots and AI Agents in n8n that query your Harvest data using Text-to-SQL and perform RAG on Harvest documents and records.
Peliqan acts as the data layer – syncing your Harvest data, converting natural language to SQL, and returning results your n8n agent can act on.
Implement a “Text to SQL” chatbot on Harvest
Implement an AI Chatbot that answers analytical questions on Harvest 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 Harvest warehouse, and returns structured results – no SQL knowledge required.
Implement a chatbot with RAG on Harvest
Implement RAG (retrieval-augmented generation) on Harvest data with Peliqan’s out-of-the-box vector store.
Peliqan automatically creates embeddings of your Harvest 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 Harvest data with 300+ sources
Combine data from Harvest with 300+ other connectors in Peliqan’s built-in warehouse.
Build unified 360° views of customers, deals, products, or employees – pulling from Harvest, 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 Harvest data for AI
Access, combine, and report on data from Harvest 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 Harvest data directly. Peliqan solves this by syncing Harvest 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 Harvest 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 Harvest data in a single context.
How to build an AI agent for Harvest ?
There are different ways to build an AI agent that can query data in Harvest and take actions in Harvest. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Harvest 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 Harvest data, combined with data from 250+ other sources.
How can I use Harvest in n8n ?
First sign up for a free trial on Peliqan.io, next connect Harvest 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 Harvest data using Text to SQL.
How to use RAG with Harvest ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Harvest data. One option is to create a workflow in n8n that fetches all Harvest 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 Harvest ?
Connect Harvest in Peliqan, then create an API handler using the built-in MCP template. Expose the Harvest 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 Harvest is never touched. The full setup takes under 10 minutes.
How do I build an AI chatbot with “Text to SQL” for Harvest ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Harvest data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Harvest data in the data warehouse.
How to create embeddings in a vector store for Harvest data ?
In order to prepare your Harvest 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 Harvest data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
All-in-one Data Platform
Built-in data warehouse, superior data activation capabilities, and AI-powered development assistance.