List of Omnious Discovery AI Customers
Seoul, 06062,
South Korea
Since 2010, our global team of researchers has been studying Omnious Discovery AI customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Omnious Discovery AI for Personalization and Product Recommendations from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Omnious Discovery AI for Personalization and Product Recommendations include: Hyundai Department Store, a South Korea based Retail organisation with 2934 employees and revenues of $1.23 billion, Ably, a South Korea based Retail organisation with 306 employees and revenues of $257.0 million, Lotte On South Korea, a South Korea based Retail organisation with 800 employees and revenues of $104.0 million and many others.
Contact us if you need a completed and verified list of companies using Omnious Discovery AI, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Omnious Discovery AI customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Ably | Retail | 306 | $257M | South Korea | Omnious | Omnious Discovery AI | Personalization and Product Recommendations | 2021 | n/a |
In 2021, Ably deployed Omnious Discovery AI on its ecommerce and shopping app. Omnious Discovery AI in the Personalization and Product Recommendations category was configured to automate attribute tagging of approximately 800,000 images and to deliver hyper-personalized visual product recommendations across the storefront.
The implementation centered on two functional capabilities, automated visual product tagging and a visual recommendation engine. The automated tagging pipeline extracted product attributes and mapped images into Ably's merchandising taxonomy, enabling downstream use by search, browse and filter components. The visual recommendation capability served image-driven, similarity and context aware product recommendations in the app, using model inference hosted alongside Omnious Discovery AI model artifacts.
Operational coverage included Ably's ecommerce and shopping app catalog and merchandising workflows, with ongoing tag curation and a feedback loop to refine attribute accuracy. Omnious Discovery AI has been linked by vendor case material to a U.S. retail fashion implementation that improved search and filter accuracy and increased purchase rate and customer session time, indicating comparable category level outcomes for visual recommendation and automated tagging deployments.
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Hyundai Department Store | Retail | 2934 | $1.2B | South Korea | Omnious | Omnious Discovery AI | Personalization and Product Recommendations | 2022 | n/a |
In 2022, Hyundai Department Store implemented Omnious Discovery AI in the Personalization and Product Recommendations category as part of its online transition to improve product metadata and recommendation quality. Omnious Discovery AI provided Visual Recommendations and Automated Product Tagging capabilities to enrich SKU attributes and to support promotion planning across the retailer's e-commerce channel.
The deployment centered on image-driven similarity modeling and an automated tagging pipeline that extracted visual attributes and appended structured metadata to product records. These functional modules fed a recommendation service that served similar-item recommendations on product pages and was configured to influence promotional assortments and on-site merchandising rules.
Operational coverage targeted Hyundai Department Store's online retail operations in South Korea, impacting e-commerce, merchandising, and promotion planning workflows. Governance included configuration of tagging taxonomies and recommendation business rules with iterative tuning during rollout. The implementation increased reported similar-item recommendation accuracy from approximately 50 percent to approximately 95 percent and raised website click-through rate by approximately 75 percent.
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Lotte On South Korea | Retail | 800 | $104M | South Korea | Omnious | Omnious Discovery AI | Personalization and Product Recommendations | 2022 | n/a |
In 2022, Lotte On South Korea deployed Omnious Discovery AI within the Personalization and Product Recommendations category to consolidate product catalogs and enrich product metadata. Omnious Discovery AI's Automated Product Tagging and Discovery Visual Recommendations capabilities were applied to consolidate seven merged catalogs and automatically tag over 1 million SKUs across the ecommerce catalog.
The implementation emphasized automated attribute extraction, visual similarity based recommendations, and attribute-driven product filters, with Omnious Discovery AI generating rich attribute data to populate taxonomy and filter facets. Configuration work focused on ingest pipelines for the merged catalog sets, automated tagging rules and model-driven attribute assignment, and recommendation model tuning to support visual discovery and filter-driven navigation.
Operational coverage centered on catalog operations, merchandising and search and browse experience for Lotte On's ecommerce site in South Korea, with the automated tagging workflow replacing manual attribute entry at scale. Governance shifted toward model supervision and exception workflows for manual review, and the rollout enabled smart product filters driven by the newly populated attributes. The project delivered explicit outcomes reported by the vendor, namely a reduction of over 90% in manual tagging resources and an increase in filter click-through rates of about 3x.
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