AI Product Discovery
Understand how AI discovers and recommends your products.
Kojable helps consumer brands understand how their products appear in AI-generated recommendations, comparisons and purchase research across Google, Meta, ChatGPT and Claude.
Measure what AI says, which competitors it recommends and where product information may be missing, inaccurate or misleading. AI Product Discovery is a capability within Kojable's AI answer alignment platform.
The free audit provides an initial Brand Summary, Associated Topics and Competitor Snapshot. Product-specific monitoring, AI surfaces and follow-up testing are scoped separately. See what the free AI Brand Audit includes.
The discovery gap
Your next customer may ask AI before visiting your website.
Customers can use AI to explore categories, find products for particular needs, compare brands, evaluate features, research suitability and shortlist purchases.
An assistant may omit your product, recommend a competitor or misunderstand an important feature. Incomplete product information can leave buyers with the wrong impression.
Kojable helps you examine those answers and identify opportunities to improve how your brand is understood. See how AI Visibility Monitoring establishes a baseline.
What Kojable measures
See how AI represents your products.
AI Product Discovery measures how assistants find, describe, compare and recommend products in a defined set of questions and testing conditions.
Product presence
Does your product appear in relevant AI answers, or is it omitted from the options presented?
Recommendation context
For which customer needs, use cases and scenarios does AI recommend your product?
Product information accuracy
Are the descriptions, features and positioning correct when checked against your product information?
Competitor comparisons
Which competing products appear, and what distinctions does AI draw between them?
Evidence and citations
Which supporting sources are referenced when available, and what evidence accompanies the answer?
Information gaps
Which important details are missing, ambiguous or inconsistent across the answers measured?
Findings depend on the question, platform, market and measurement time. They describe observed answers, not a guarantee of future recommendations. Learn how Kojable investigates citations and source patterns.
Illustrative measurement questions
The questions your customers might ask AI.
Examples for planning a measurement set. These are not recorded AI responses or customer results.
- What are the best [product category] options for [specific customer need]?
- How does [your product] compare with [competitor product]?
- Which products include [important feature]?
- Is [your product] suitable for [specific use case]?
- Which brands offer [desired characteristic] in [category]?
Platform coverage
One view across the AI assistants that matter.
Our four platform groups are Google, Meta, ChatGPT and Claude. Exact surfaces, models, markets and testing conditions are confirmed in each engagement's written scope.
Google- Google includes Gemini, AI Mode and AI Overviews. These are distinct experiences with different access and measurement conditions. Coverage of one does not imply coverage of all three.
Meta- We measure grounded Muse Spark answers through Meta’s API. Kojable uses Muse Spark through the Meta Model API with web-search grounding. This measures the grounded responses available through the integration, not private or personalised AI conversations inside Instagram, WhatsApp or Facebook.
ChatGPT- Product-related answer testing with the model, access method and response conditions recorded. OpenAI identifies the API provider where relevant; API testing does not imply access to every ChatGPT application mode or subscription tier.
Claude- Product-related answer testing with the model, access method and response conditions recorded. Anthropic identifies the API provider where relevant; included Claude models and surfaces are agreed for the engagement.
Monitor → Diagnose → Improve → Verify
From AI answers to actionable improvements.
Monitor
Measure product-related questions and AI answers across the platforms, markets and surfaces in scope.
Diagnose
Identify omissions, inaccuracies, competitor positioning and source gaps. An observed citation does not prove what caused an answer.
Improve
Prioritise product information, content, positioning and supporting evidence that may need work. Your team reviews and approves changes; implementation responsibilities are agreed separately.
Verify
Retest relevant questions under comparable conditions. Compare answers over time and document what changed and what still needs attention.
Explore the full Kojable workflow and see how the product capabilities connect.
Who it is for
For teams responsible for product understanding.
Consumer and ecommerce brands
Understand product discovery and competitive recommendation patterns across the questions that matter to your category.
Creator-led businesses
See how AI describes your products, collections or creator-led brand, including the details that distinguish your offer.
Growth and marketing teams
Prioritise discovery gaps and measure how answer quality changes after agreed improvements.
Discovery and commerce
Where AI Product Discovery fits into agentic commerce.
AI Product Discovery concerns how assistants find, describe, compare and recommend products.
Agentic commerce is the broader process in which AI agents may assist with shopping decisions and transactions.
Kojable focuses on measuring and improving the discovery and recommendation layer. Kojable does not process purchases, control shopping carts, execute checkout or manage inventory and fulfilment. It cannot guarantee that an assistant will recommend a product or that an agent will buy it.
Practical answers
Frequently asked questions about AI Product Discovery
What is AI Product Discovery?
AI Product Discovery is how assistants find, describe, compare and recommend products during purchase research. Kojable measures these answers to help brands understand product presence, accuracy, competitive framing and missing evidence.
How does Kojable measure AI product recommendations?
Kojable tests product-related questions and examines the resulting answers under recorded conditions. Findings cover product presence, recommendation context, information accuracy, competitors, available citations and information gaps, with the platform, market and measurement time kept in context.
Which AI platforms does Kojable cover?
Kojable's platform focus is Google, Meta, ChatGPT and Claude, with exact coverage agreed in each engagement. Gemini, AI Mode and AI Overviews are distinct Google experiences; Meta measurements use grounded Muse Spark answers through the Meta Model API with web-search grounding.
Does Meta coverage include personalized AI answers inside Instagram, WhatsApp, and Facebook?
No. Kojable measures grounded Muse Spark answers through Meta’s API, not private or personalised AI conversations inside Instagram, WhatsApp or Facebook. The measurement covers the grounded responses available through the integration.
Is AI Product Discovery the same as agentic commerce?
No. AI Product Discovery concerns product discovery and recommendations, while agentic commerce may also involve shopping decisions and transactions. Kojable focuses on measuring and improving the discovery layer; it does not process purchases or execute checkout.
Can Kojable guarantee that AI assistants will recommend my products?
No. Kojable cannot guarantee rankings, citations, recommendations, conversions or purchasing outcomes. It measures observed answers, helps prioritise improvements and retests comparable questions without controlling third-party AI systems.
What can a brand do when Kojable identifies incorrect or missing product information?
A brand can review the finding against authoritative product information and prioritise corrections to product pages, supporting content or evidence. Kojable helps explain what needs attention; your team approves changes, with implementation and follow-up testing defined in the agreed scope.