San Francisco, 3 March 2026 – Meta is quietly testing an AI-powered shopping research assistant designed to compete with generative AI offerings such as ChatGPT and Gemini, according to people familiar with the matter.
The tool, which is currently in limited trials with select users, utilises large-language-model technology to help consumers gather personalised product information, compare options and assist with purchase decisions, effectively acting as a conversational shopping advisor embedded within Meta’s ecosystem.
Bringing AI Deeper Into E-Commerce Experiences
Unlike conventional search or recommendation engines, Meta’s prototype aims to generate tailored answers to natural-language queries about products, pricing, features and shopping preferences. For example, users could ask the assistant to summarise reviews for a specific item, explain differences between models, or suggest gift ideas based on a set of criteria.
The integration of such an AI assistant could deepen Meta’s presence in e-commerce, a strategic priority as the company seeks to diversify revenue beyond advertising and attract more shopping-focused engagement on its platforms.
Competitive Landscape With AI Models
Meta’s shopping AI is being positioned as a competitor to general-purpose AI models developed by rivals. ChatGPT and Gemini have both added features that facilitate shopping research, including summarised product comparisons and conversational interfaces tied to web knowledge.
Industry analysts say Meta’s advantage could come from its access to vast user behaviour signals across its social platforms and Marketplace, enabling more context-aware recommendations. However, the company will need to build trust around data usage, privacy safeguards and accurate content generation, key considerations that have tripped up earlier AI initiatives.
User Testing and Monetisation Prospects
Sources familiar with the tests say Meta’s prototype is still in early stages and may evolve significantly before a full rollout. Initial user experiences reportedly emphasise conversational research support, but Meta could look to monetise the feature through affiliate partnerships, product tagging in feeds, or integration with existing Shops listings.
The move aligns with broader industry trends in which AI is increasingly being used to blur the lines between discovery, research and purchase, turning passive browsing into a more interactive, advice-driven shopping experience.
Challenges Ahead
Meta faces a crowded competitive landscape as technology companies race to embed AI into commerce workflows. Google’s Gemini and Microsoft-backed models like Copilot AI already offer dynamic product research and comparison capabilities, while Amazon and Shopify are investing in AI tools to enhance discovery and personalised recommendations.
Moreover, regulatory scrutiny over data privacy and platform-generated recommendations could shape how Meta and others deploy AI in retail contexts. Ensuring transparency in how suggestions are made, and how user data is utilised, will be essential for building consumer trust.
