Use cases

Roblox's platform team utilizes Milvus to support a variety of internal use cases, with the most significant being avatar search, leveraging Milvus' vector search capabilities to efficiently match users with avatar options, enhancing the customization and user experience on the platform.

Salesforce's platform team uses Milvus to support a wide range of internal use cases, serving 100+ tenants with diverse applications and varying service levels, leveraging Milvus' vector search technology to enhance functionality and performance across their extensive ecosystem.

Otter.ai leverages Milvus for Retrieval-Augmented Generation (RAG) to enhance Q&A functionality by efficiently sourcing and referencing relevant information from meeting transcripts, improving access to key insights and answers directly from recorded discussions.

Palo Alto Networks utilizes Milvus for fraud detection, employing its vector search capabilities to analyze and identify patterns indicative of fraudulent activity, thereby enhancing the security and integrity of their networks and services.

Poshmark employs Milvus for product recommendation, utilizing vector search to analyze user behavior and preferences, efficiently matching buyers with relevant fashion items, thereby personalizing and enhancing the shopping experience.

Chegg employs Milvus for storing document chunks and embeddings to enhance document search capabilities, powering the homework help chat feature on chegg.com, thereby streamlining access to educational resources and assistance.

Shell employs Milvus for multiple applications, primarily focusing on document search within a Retrieval-Augmented Generation (RAG) context, utilizing Milvus' vector search technology to enhance the accessibility and retrieval of documents in their vast corporate knowledge base.

Compass utilizes Milvus for custom search by vectorizing floorplans, enabling the search for homes beyond traditional keyword matches and enhancing the property discovery process with advanced spatial and feature-based queries.

AT&T leverages Milvus for document search, utilizing its vector search capabilities to fetch the most relevant semantic results for customers, significantly enhancing the accuracy and efficiency of information retrieval.

Tokopedia upgraded its product search and ranking by adopting Milvus for vector similarity search, enhancing semantic understanding and efficiency over Elasticsearch, resulting in a smarter, stable, and reliable Ads service with significantly improved click-through and conversion rates.

Mozat leverages Milvus for Stylepedia's image search system, utilizing its capability for real-time, large-scale vector similarity searches across billions of datasets for garment detection, feature extraction, and refined post-processing, enhancing user experiences with functions like searching for similar clothing items, outfit suggestions, and personalized fashion recommendations.

Line utilizes Milvus to power its user-generated content (UGC) recommendation engine, enabling personalized news and music suggestions based on users' existing preferences through advanced vector search technology.

SmartNews leveraged Milvus for its high-throughput, low-latency vector similarity search capabilities to efficiently match users with relevant ads in real-time, significantly enhancing ad recommendation performance and scalability.

Farfetch employs Milvus as a vector database to support iFetch, its conversational AI for personalized shopping, by enabling real-time, accurate product recommendations through machine learning-generated product embeddings.

Walmart employs Milvus across multiple use cases, with its platform team leveraging the vector database to support various internal applications, including enhancing product search capabilities by efficiently matching consumer queries with relevant product listings through advanced vector search technology.

IKEA utilizes Milvus for product recommendation, leveraging vector search technology to analyze customer preferences and match them with the most relevant products, enhancing the shopping experience through personalized suggestions.

HumanSignal, formerly Heartex, is a leading provider of AI data labeling solutions and is known for its open-source platform, Label Studio. It integrates Milvus into its Label Studio platform to enhance data discovery and labeling processes. Milvus's support for various indexing algorithms improves semantic search efficiency, enabling users to identify relevant data subsets swiftly. Deployed on AWS using Elastic Kubernetes Service (EKS), this integration ensures scalable and reliable data management, significantly boosting model accuracy and development speed.

Omers' Data Science/Data Engineering team is utilizing Milvus to create a semantic search solution for financial documents, harnessing Milvus' vector database capabilities to efficiently index and query complex financial data for improved insight and retrieval.

ZipRecruiter leverages Milvus to enhance the recruitment process by embedding candidates and job opportunities in the same vector space, enabling efficient and accurate matching between job seekers and relevant positions.

Grab employs Milvus for its food and restaurant recommendation system, utilizing vector search to analyze customer preferences and match them with relevant dining options, enhancing the user experience through personalized suggestions.

Shutterstock enhances its "Search by image" feature by leveraging Milvus, enabling reverse image search capabilities that efficiently match user-uploaded images with similar content in their vast digital asset library.

Zigram employs Milvus for fraud detection by comparing real-time transactions against a database of known fraudulent activities, utilizing vector search technology to quickly and accurately identify potential fraud, enhancing security and trust in their operations.

VerSe employs Milvus to enhance its recommendation engine for short videos by utilizing vector search to align viewer preferences with relevant video content, thus improving user engagement through personalized content suggestions.

Bosch uses Towhee and Milvus to enhance its manufacturing process by detecting defects in images, leveraging the combined power of Towhee's data processing and Milvus' vector search capabilities for improved quality control.

VIPShop utilizes Milvus for its recommendation system, transforming product features and user behaviors into vector embeddings for fast, accurate similarity searches, outperforming traditional solutions with features like distributed deployment and multi-language SDKs.

Shopee employs Milvus to enhance its real-time search capabilities, particularly in video recall, copyright matching, and video deduplication systems, leveraging Milvus' ability to efficiently handle billions of vectors and scale with data volume, thereby improving user experience and maintaining content integrity.

PayPal utilizes Milvus for its impressive performance and scalability in handling large-scale data and AI use cases, starting with a recommender system and expanding to a multilingual customer service chatbot

Airbnb utilizes Milvus for its text-to-image search feature, enabling users to find rental listings by describing their desired features, such as "modernist building near the forest," through efficient vector search capabilities that match descriptive text with relevant property images.

Credal AI deployed Milvus on Amazon EKS to power semantic search in GenAI-driven workflows, enabling scalable, real-time processing of large datasets across diverse hosting environments, including cloud and on-prem setups.

Landing AI, founded by Andrew Ng, is a pioneer in Visual AI solutions, empowering industries to harness the full potential of visual data. By leveraging Milvus for image-based object detection, Landing AI enhances quality control in semiconductor manufacturing, specifically detecting disinfection in chip production processes. Using domain-specific Large Vision Models (LVMs) and Large Multimodal Models (LMMs), the platform ensures high accuracy and consistency in AI-driven inspections, transforming proof-of-concept AI projects into production-ready solutions.

TrendMicro adopted Milvus as a scalable, flexible vector search engine that overcame the limitations of MySQL and Faiss, enhancing APK security analysis with its advanced integration capabilities, intuitive API, and robust monitoring features, improving query performance and system scalability.

Sohu uses Milvus for semantic searches, optimizing news recommendations and classification to boost user experience and content relevance.

Bigo leverages Milvus for its efficient video deduplication system, enabling rapid similarity searches and high recall rates for its vast video content, significantly improving query throughput and operational efficiency by using Milvus' capabilities to handle and index large-scale vector data.

Deepset incorporates Milvus into their Haystack framework to boost semantic search capabilities. Milvus enhances data indexing and similarity searches, supporting dynamic data management and integrating with multiple Approximate Nearest Neighbours libraries.

The European Patent Office utilizes Milvus for patent search, leveraging its vector search capabilities to efficiently match queries with relevant patents, enhancing the precision and speed of patent retrieval and analysis.

Zomato utilizes Milvus for its restaurant search functionality, leveraging vector search to analyze and match user queries with the most relevant dining options, enhancing the discovery process for users looking for specific restaurant experiences.