OpenSearch Vector Engine

Create applications that allow users to take a photograph and search for similar images without having to manually tag images.

Enhance search relevancy by powering vector search with text embedding models that capture semantic meaning and use hybrid scoring to blend term frequency models (Okapi BM25) for improved results. To learn more, see Semantic search.

Use state-of-the-art models that can fuse and encode text, image, and audio inputs to generate more accurate digital fingerprints of rich media and enable more relevant search and insights. To learn more, see Multimodal search.

Build intelligent agents powered by generative AI while minimizing hallucinations. Use OpenSearch to enhance retrieval-augmented generation (RAG) workflows with large language models (LLMs) for more accurate and context-aware responses. To learn more, see Generative AI agents.

Generate product and user embeddings using collaborative filtering techniques and use OpenSearch to power your recommendation engine. Enhance customer experiences by providing relevant repeat purchase, try new, and cold start recommendations.

Personalize web pages by using OpenSearch to retrieve content ranked by user propensities using embeddings trained on user interactions.

Deliver personalized shopping experiences by suggesting similar products based on a specific item, and guide customers to relevant products based on their browsing history or past purchases, powering “You might like this because you bought/viewed/clicked on this” recommendations. OpenSearch Vector Engine generates rich item embeddings optimized for predicting which products are likely to be purchased together, enhancing engagement and conversion rates.

Improve product ranking by combining traditional text-based search algorithms like Okapi BM25 with vector search capabilities. OpenSearch Vector Engine draws on historical business data to optimize search rankings, going beyond simple language similarities to deliver highly personalized results, boosting relevance and increasing the likelihood of conversion.

Optimize growth by predicting customer churn, identifying users at risk of unsubscribing or stopping purchases. Take proactive steps, such as personalized notifications, discounts, or special offers, to re-engage and retain these users. With OpenSearch Vector Engine, you can build powerful models to effectively predict and prevent churn, driving long-term customer loyalty.

Predict customer lifetime value (LTV) to optimize marketing, strengthen customer relationships, and maximize profitability. Forecast future revenue, refine strategies and allocate resources more efficiently, focusing on high-value customers. With OpenSearch Vector Engine, you can build accurate models to predict LTV and drive smarter decision-making.

Increase email engagement with personalized content that drives active user counts and order rates. OpenSearch Vector Engine enables fast deployment of advanced recommendation models, enhancing email campaigns with tailored suggestions that deliver measurable business results.

Analyze time-series and sensor data to predict when equipment needs repair or servicing, preventing costly downtime and repairs. By storing sensor data as vector data and using ML algorithms, OpenSearch Vector Engine helps analyze equipment data to predict potential failures before they happen, enabling timely
maintenance and avoiding expensive breakdowns.

Surface defects in real-time during the manufacturing process to ensure product quality and reduce waste. OpenSearch Vector Databases’ built-in anomaly detection capabilities help identify issues as they occur, enabling immediate action to maintain high standards and improve efficiency.

Tackle a wide range of fraud and abuse challenges, from money laundering and credit card fraud to insurance and return fraud. OpenSearch Vector Engine’s built-in anomaly detection capabilities help organizations identify suspicious activity with precision and efficiency, enhancing security and reducing risk.