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Signals

An AI store associate that texts customers over iMessage for DTC brands

Signals provides an AI store associate that texts customers over iMessage for DTC brands.

Overview

Signals sells an AI store associate that talks with a brand's customers over iMessage, RCS/SMS, WhatsApp and Instagram. It answers product, sizing, order and return questions and follows up after delivery. It is meant to help merchants support customers and bring them back to buy again.

For Shopify brands

  • Answers sizing, product, care and order questions and handles exchanges in one ongoing iMessage thread with each customer.
  • Escalates unusual conversations to a human in the same thread, so the team can step in with context.
  • Connects to Shopify, returns, helpdesk, marketing and fulfillment tools, and runs as an A/B test against a holdout to measure repeat revenue.

How it works

Signals keeps one conversation open with each customer before the order, during the order, after delivery and ahead of the next purchase. Most conversations resolve with AI, and unusual ones go to the brand's team in the same thread through a dashboard. Results are measured with an A/B test against a holdout group.

Built for
DTC brands that grow through customer relationships and repeat purchases, with examples in apparel and other product retail.

Products

  • Signals AI store associate that texts customers about products, orders, exchanges and next purchases over iMessage.

Integrations

iMessageRCS / SMSWhatsAppInstagramShopifyLoopRedoStay.aiSubscribfyGorgiasZendeskGladlyListrakPostscriptRivoOrder DeskManhattan AssociatesEasyPostSanityGoogle Sheets

Buyer notes

  • Site lists more integrations behind a 'Show all integrations' link.
  • When AI meets an unusual case, it escalates to a human in the same thread.
  • Results are described as measured in A/B tests against a holdout group.
  • Site offers a demo booking, an ROI calculator and a compliance page.

From Signals's own site and Shopify App Store listings, as of 2026-11-03.

Updated 2026-09-10. Method notes and every source: how we count →