Product discovery solutions help users find relevant products or content through search, recommendations, and natural-language queries. They address difficulties in locating items quickly across large catalogs and in matching shoppers with suitable options.
Use cases
14
Examples
14
Industries
3
Timeline
7 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
12 cases documented across 37 months (Jul 23 – Jul 26), peaking at 4 in June 2026.
AI Use Cases Hub
1 earlier case before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
3.7Innovativeness3.7/5Advanced3.7/5 - Advanced. More advanced than a basic chatbot or simple RAG because it combines multimodal catalog generation, image enhancement, multilingual semantic search, and operational workflow automation; comparable to other recent Google Cloud retail AI implementations rather than frontier novelty.
Mystore, an ONDC ecommerce marketplace, uses Google Cloud AI to boost efficiency and connect millions of buyers and sellers.The platform automates product cataloging and approvals at scale for India’s small and medium-sized sellers, helping them create and maintain accurate listings.Mystore also uses AI for multilingual semantic search, conversational shopping, and support workflows across its seller and buyer experiences.
3.4Innovativeness3.4/5Differentiated3.4/5 - Differentiated. More novel than a basic chatbot because it combines customer search, supplier discovery, OCR extraction, and analytics acceleration in one retail workflow. Similar recent Vertex AI commerce cases were scored around 3 to 3.5, so this fits the differentiated range rather than advanced.
Moglix is an India-based industrial ecommerce platform that used Google Cloud Vertex AI to improve product search, automate product descriptions, answer customer questions, and help executives discover information faster.The implementation also used OCR to extract structured product data from PDFs and BigQuery to accelerate reporting and analysis.
3.2Innovativeness3.2/5Differentiated3.2/5 - Differentiated. This is a practical multi-use Vertex AI ecommerce deployment with search, chatbot, and OCR, but it is still an applied business workflow case similar to many recent Google Cloud customer stories rather than a distinctly novel architecture.
Moglix rendered digital transformation and innovation for its clients with Google Cloud's Vertex AI, enhancing product searches, descriptions, data access, and efficiency to improve customer experiences.The company uses Vertex AI for unified natural-language search, automated product descriptions, chatbot responses, corporate information discovery, and OCR-based extraction from PDFs.
2.4Innovativeness2.4/5Incremental2.4/5 - Incremental. Compared with recent Gemini/Vertex AI retail discovery cases, this is a solid but not frontier architecture: multimodal classification, embeddings, vector search, and automated pipelines are all well-established patterns. The scale is strong, but the technical pattern is similar to other recent applied GenAI commerce implementations.
GoWish is a global digital wishlist and social shopping platform.It built an AI-powered aggregation layer on Google Cloud to group similar product links into AI-generated products, enrich product profiles, and recommend alternatives when items are out of stock.
3.2Innovativeness3.2/5Differentiated3.2/5 - Differentiated. Compared with recent Google Cloud retail AI cases, this is a differentiated but still applied implementation: conversational and multimodal search plus LINE integration and personalization, but not a fundamentally new architecture.
Shop Global, part of Thailand's Saha Group, implemented an AI-powered agentic and LLM search solution to replace keyword search with conversational and multimodal product discovery.The solution supports text and photo search, extracts product attributes, and uses user profile, session, trend, and weather context to generate hyper-personalized recommendations through a chatbot and dynamic results page.It is integrated into the LINE shopping experience as an AI Personal Shopper for personalized deals and in-app shopping, and supports Thai and English search at high event traffic.
4Innovativeness4/5Advanced4/5 - Advanced. Advanced architecture with scalable multimodal video AI models integrated on AWS infrastructure enabling petabyte-scale vector search with high reliability and cost efficiency.
TwelveLabs developed breakthrough multimodal video AI foundation models, Marengo and Pegasus, that understand video as unified stories across sight, sound, and time, enabling powerful video search and analysis.To deploy these models at production scale, TwelveLabs leveraged AWS infrastructure, including Amazon Bedrock, Amazon SageMaker HyperPod for model training resilience, Amazon EKS for scalable model serving, EC2 G6e and P5 instances for inference, and Amazon S3 with S3 Vectors for unified storage and vector-based semantic search.This unified AWS platform supports petabyte-scale video indexing and sub-second search across billions of vector embeddings, delivering reliable, cost-efficient video intelligence at unprecedented scale.TwelveLabs' solution transforms massive unstructured video archives into searchable, analyzable assets, powering applications across media, advertising, manufacturing, and government sectors.
2Innovativeness2/5Incremental2/5 - Incremental. The assistant uses Azure OpenAI with semantic search integrated into Salesforce knowledge and human feedback, which is a standard support QA/search automation approach without evidence of advanced architecture.
Polestar, a Swedish electric car brand, improved customer service efficiency by deploying an AI-driven assistant named CAIR. Built on Microsoft Azure OpenAI with Semantic Search and integrated into the company's Salesforce knowledge base, CAIR analyzes incoming queries, searches historical support cases and internal knowledge, and generates contextually appropriate responses. Human agents review and improve the AI’s answers over time, continuously enhancing quality and tone. The system has led to faster response times, increased customer satisfaction, and significant agent time-savings, positioning Polestar as a leader in AI-driven automotive customer experience innovation.
3Innovativeness3/5Differentiated3/5 - Differentiated. Application of Vertex AI Search for Commerce to replace legacy rule-based search, leveraging ML for improved search relevance across multiple channels.
Toolstation, a UK-based retailer, improved product search accuracy across its website, app, contact center, and physical stores using Google Cloud's Vertex AI Search for Commerce.The challenge was customers struggling to find products due to synonyms, slang, and spelling errors, leading to lost sales and poor reviews.They migrated from a rule-based legacy search tool to an ML-driven Vertex AI Search that dynamically understands customer search terms and continuously learns from search behavior.Implemented search integration in contact centers and in-store tills to assist staff in finding products faster and more accurately.Results included reducing zero-result searches from 2% to 0.1%, a 10% higher click-through rate, 5% increase in search-based revenue, and improved customer loyalty.
2Innovativeness2/5Incremental2/5 - Incremental. Introduces generative AI themed product search and an AI workforce assistant with document summarization, plus cart-scanning vision, which is useful but largely follows common retail AI patterns.
Walmart has expanded its generative AI search tool, allowing shoppers to search for products by themes and use cases on its app and website, powered by Microsoft Azure OpenAI Service.This innovation enables users to request themed product bundles instead of searching for individual items, such as all needed items for a football watch party.The generative AI tool leverages natural language processing to return a selection of relevant products from different categories, improving the user experience.Walmart has invested in automation across warehouses and revamped its app and site to capitalize on AI-driven efficiency.Additionally, the retailer launched 'My Assistant', a generative AI-powered tool for workforce productivity, helping employees create drafts and summarize documents.'My Assistant' supports operations in 11 countries with multi-language capabilities, reducing manual workload and increasing efficiency.Walmart also showcased advancements in AI-powered cart scanning at Sam’s Club and expanded its technology ecosystem with drone delivery initiatives.The project is part of Walmart's broader strategy to drive engagement, boost sales, and scale operational improvements using automation and generative AI.The global rollout indicates a commitment to a data-driven, scalable approach in retail operations.Implementation highlights included a keynote at CES 2024 by Walmart and Microsoft leadership, underlining strategic partnership and technological leadership.
2Innovativeness2/5Incremental2/5 - Incremental. It implements GenAI-powered natural-language search for product recommendations using Azure OpenAI, but the architecture and integration details provided do not indicate unusual technical innovation.
Walmart implemented Microsoft Azure OpenAI Service to create a Generation AI-powered search feature within its shopping app. The feature utilizes advanced natural language capabilities to analyze contextual customer queries, delivering more precise and relevant product recommendations. This advancement aims to redefine the shopping experience by providing a user-friendly, intelligent interface that adapts to individual customer needs.
How many product discovery use cases are documented?
The AI Use Case Hub documents 14 real product discovery deployments across 3 industries, with 14 detailed company examples you can browse.
Which industries adopt product discovery the most?
Product discovery is most common in Retail (79%), Tech & Comms (14%) and Automotive (7%).
Which countries lead in product discovery?
United States leads documented product discovery deployments, followed by India and Thailand.
What technologies are used for product discovery?
Teams most often build product discovery with Vertex AI, Gemini and BigQuery.
What AI capabilities power product discovery?
Across the documented deployments, the most common capability patterns are Vision (43%), Agent (21%) and Fine-tuning (7%).
What results do companies report from product discovery?
Across the 14 deployments reporting outcomes, companies most often cite new product / capability (93%), customer experience & trust (79%) and speed & agility (36%). Where impact is quantified, the strongest evidence is in revenue & growth: a median +12.5% across 4 reported metrics.
