USE CASE · META AI
Meta AI meets the shopper inside the apps where discovery already happens.
Meta AI answers product questions inside Instagram, WhatsApp and Facebook - the same apps where your paid social already runs. Tru Commerce measures your presence there and connects the catalog behind it.
Meta AI is the assistant embedded across Meta's apps. It sits directly beside the discovery and paid-social motion brands already invest in, which makes catalog quality and assistant visibility the same problem.
PROOF
7×
MyMuse's real AI-driven revenue versus what GA4 reported - ₹81.2K/mo actual against ₹11.5K/mo attributed (Tru Commerce case study)
On Meta AI
Discover. Recommend. Transact.
01 · Discover
See whether Meta AI surfaces your products in the categories you already advertise in.
02 · Recommend
Align the commerce catalog and product data the assistant reads.
03 · Transact
Connect checkout so an in-app recommendation converts.
What powers it
The Tru Commerce products behind Meta AI
How Meta AI works
What decides whether it recommends you
It sits inside the discovery apps
Unlike a standalone assistant, Meta AI is reached mid-scroll in Instagram or mid-thread in WhatsApp. Intent is often earlier and broader than a search query, which rewards strong category and use-case coverage.
Your commerce catalog is the substrate
Product data already syndicated to Meta for shopping and ads is the same data the assistant can draw on. Feed quality work pays into both motions at once.
Conversational discovery favours use cases
Questions arrive as situations - a gift, an occasion, a problem to solve - rather than product names. Catalogs described only by specification miss those matches.
It overlaps your existing paid spend
The same audience your social budget reaches can now get an organic answer. Being absent from that answer means paying for reach you could hold on merit.
Case study
GA4 showed ₹11.5K/mo. Real AI revenue: ₹81.2K/mo. 7× hidden
FAQ
Common questions
Meta AI is the assistant built into Instagram, WhatsApp, Facebook and Messenger. Shoppers ask it questions in natural language, including product and recommendation questions, without leaving the app.