Harper's release of version 4.6 of its composable application platform features vector indexing to efficiently manage high-dimensional vector data, crucial for AI models like smart search. This enhancement aids large brands, accelerating customer journeys and increasing purchases. A study reveals 62% of shoppers prefer AI recommendations, with 68% among millennials; poor search experiences lead 72% to abandon sites. Harper's architecture, integrating various functions into one runtime, offers low-latency performance by processing data at the edge, enhancing user satisfaction and revenue growth.
The latest release features several enterprise-grade components to improve performance and maximize revenue at any scale, chief among them the addition of vector indexing for the efficient storing and retrieving of high-dimensional vector data.
A new study on shopper expectations found 62% of respondents are more likely to buy when guided by AI-powered recommendations. Among millennials, that number jumps to 68%.
Harper's low-latency architecture and superior performance capabilities are attractive features for large digital brands with high-volume websites.
The vector indexing feature found in Harper v. 4.6 powered by the Hierarchical Navigable Small World (HNSW) algorithm, allows for quick and accurate nearest-neighbor search.
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