---
title: "Frate Returns Integration for Rig: Query Frate Returns Data in Plain English"
description: "Connect Frate Returns to Rig and query your Frate Returns data in plain English, or ask it in Claude, ChatGPT and Cursor. Governed, validated answers from your warehouse."
canonical: "https://rig.so/integrations/frate-returns"
format: markdown
---

Browse integrations

![Frate Returns logo](https://img.logo.dev/frateapp.com?token=pk_AdkTbLWDS1iunmvTK2qm1g&size=112&format=png&retina=true)

E-commerce & marketplaces Supported

# Frate Returns integration for Rig

## The easiest way to connect Frate Returns to Claude and other AI tools

Frate Returns manages ecommerce returns, covering the return request flow, shipments back and the reasons customers give.

[Try Rig](https://app.rig.so/signup)[Book a demo](https://rig.so/book-demo)

## What Rig syncs from Frate Returns

Once connected, Rig models these objects so questions resolve to the right tables and joins:

- Return groups
- Return shipments
- SKU-level return line items
- Return reason taxonomy
- Return amounts
- Allowlist policy items

## How Frate Returns connects to Rig

Give Rig an API token. Rig syncs return groups with their shipments and SKU-level return line items, including the return-reason taxonomy and amounts, plus allowlist policy items, so returns join to the orders and products they came from.

## Find the products returns are eating

Return rate at the company level hides everything useful. At SKU level, joined to sales, it shows which products lose money after returns and which sizes or variants are the actual problem.

Ask Rig

- Which SKUs have the highest return rate relative to units sold?
- What is net revenue after returns by product?
- Which variants are returned most, and for what reason?

## Act on return reasons

The reason taxonomy is the most actionable field in returns data and the least used, because it sits in a separate tool. In the warehouse you can group reasons by product and category, which separates a sizing problem from a quality problem from a photography problem.

Ask Rig

- What are the top return reasons by category?
- Which products are returned mainly for sizing?
- Has the reason mix changed since we updated the size guide?

## Understand what returns cost

Return amounts alongside order values and shipping costs give the real figure, which is usually worse than the refund alone. That makes policy decisions such as free returns or restocking fees measurable rather than instinctive.

Ask Rig

- What did returns cost us last quarter including shipping?
- Which customer segments return most, and are they still profitable?
- How would a restocking fee change contribution by category?

## Ask your Frate Returns data in Claude, ChatGPT or Cursor

Rig serves your Frate Returns data to AI assistants over MCP. Once Frate Returns is synced into your warehouse, you can ask questions in plain English from the tool you already use, and every answer is backed by validated, governed SQL.

- [Claude](https://rig.so/integrations/claude)
- [Claude Code](https://rig.so/integrations/claude-code)
- [Cursor](https://rig.so/integrations/cursor)
- [ChatGPT](https://rig.so/integrations/chatgpt)
- [Codex](https://rig.so/integrations/codex)
- [Gemini](https://rig.so/integrations/gemini)

## Frequently asked questions

What does Rig sync from Frate Returns?

Return groups with their shipments and SKU-level return line items, including the return-reason taxonomy and amounts, plus allowlist policy items.

Can I calculate net revenue after returns?

Yes. With returns at SKU level beside your orders, net-of-returns revenue and margin can be computed per product rather than estimated at company level.

What do I need to connect Frate Returns?

An API token from your Frate account.

Does it include why customers returned?

Yes. The return-reason taxonomy comes across, which is what makes returns data useful for product decisions rather than just for finance.

## Related integrations

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## Connect Frate Returns data to AI tools like Claude

Rig builds a governed context layer over your data so every team, and every AI tool, asks questions and gets answers they can trust.

[Try Rig](https://app.rig.so/signup)[Book a demo](https://rig.so/book-demo)

---

- [This page as HTML](https://rig.so/integrations/frate-returns)
- [Site map for language models](https://rig.so/llms.txt)
- [API and agent documentation](https://rig.so/developers)
