Build AI Agents on Google Document AI
Build intelligent AI Agents on top of Google Document AI with Peliqan, the leading data foundation for the Agentic AI world.
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- 5-minute setup
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Build AI Agents on top of Google Document AI
Build intelligent AI Agents on top of Google Document AI, with Peliqan’s AI data foundation:
- Google Document AI MCP Server
- Google Document AI in n8n AI Agents
- Google Document AI in Make
- “Text to SQL” on Google Document AI data
- Google Document AI RAG with out-of-the-box embeddings (vectors)
- Google Document AI Graph RAG
- Query unified 360° data combining Google Document AI and other data
Google Document AI MCP Server
Publish a Google Document AI MCP Server to query data from Google Document AI and to take actions in Google Document AI such as doing updates and adding new data in Google Document AI.
Google Document AI in n8n
Build AI Chatbots and AI Agents in n8n that can perform “Text to SQL” to query Google Document AI data and perform RAG and Graph RAG on information from Google Document AI.
Implement a “Text to SQL” chatbot on Google Document AI
Implement an AI Chatbot that can answer analytical data questions on Google Document AI data using “Text to SQL”.
Implement a chatbot with RAG on Google Document AI
Implement RAG (retrieval-augmented generation) on Google Document AI data with an out-of-the-box vector store (embeddings) of all business entities and other information in Google Document AI.
Combine Google Document AI data with 250+ sources
Combine data from Google Document AI with data from 250+ other connectors, and build 360° views of business entities such as customers, leads, products, employees etc.
Feed unified 360° data models to your AI Agents with RAG and “Text to SQL”. Allow your AI Agents to access all business data in one uniform data model.
Prepare your Google Document AI data for AI
Access, combine, and report on data from Google Document AI 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 ?
Peliqan is an all-in-one data platform with 250+ data connectors (ERP, CRM, Accounting, ATS/HRM, cloud storage etc.) – including Google Document AI – and a built-in data warehouse. Peliqan allows you to unleash, prepare and combine your Google Document AI data for AI, including relational & non-relational data. Peliqan turns your Google Document AI data into 360° views that can be used in AI Agents built in n8n, Make, langChain, langGraph or any other framework. Use Peliqan to create embeddings, store them in a vector store so that your AI chatbots can use RAG and Graph RAG, combined with Text-to-SQL for analytical reasoning. Peliqan is the only platform that allows your AI Agents to combine RAG and Text-to-SQL to apply deep reasoning on your Google Document AI data. Use Peliqan to expose any Google Document AI as an MCP server to query data and to take actions.
How to build an AI agent for Google Document AI ?
There are different ways to build an AI agent that can query data in Google Document AI and take actions in Google Document AI. For example you can build an AI agent in n8n and use Peliqan as the data foundation. Peliqan will sync your Google Document AI 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 Google Document AI data, combined with data from 250+ other sources.
How can I use Google Document AI in n8n ?
First sign up for a free trial on Peliqan.io, next connect Google Document AI 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 Google Document AI data using Text to SQL.
How to use RAG with Google Document AI ?
There are different options to use RAG (retrieval augmented generation) in your AI Agent with Google Document AI data. One option is to create a workflow in n8n that fetches all Google Document AI 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 Google Document AI ?
In Peliqan, you can set up API endpoints and expose them as MCP Server. In the API endpoint handler script, you can configure actions to be taken in Google Document AI such as querying data, doing lookups, adding new items or performing updates.
How do I build an AI chatbot with “Text to SQL” for Google Document AI ?
n8n is a great tool to build AI chatbots that use Text to SQL, to answer any analytical question on your Google Document AI data. Any question will be converted by the AI agent into an SQL query, which is executed by Peliqan on the Google Document AI data in the data warehouse.
How to create embeddings in a vector store for Google Document AI data ?
In order to prepare your Google Document AI 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 Google Document AI data from Peliqan and stores it in Supabase as a vector store, with embeddings created using e.g. OpenAI.
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