Vector Indexing for AI-powered search

Harper, bringing next-level web performance to a digital-first world, recently announced the release of version 4.6 of its composable application platform. 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 – essential for bringing contextual depth to AI models like smart search.

For large digital brands with extensive product catalogues, the introduction to AI-enhanced search helps accelerate the customer’s journey and time-to-purchase. 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%. Conversely, bad search experiences drive shoppers away, with 72% of consumers abandoning sites due to poor search.

Harper now features Vector Indexing for AI-powered search

Harper’s low-latency architecture and superior performance capabilities are attractive features for large digital brands with high-volume websites. The composable application platform integrates a high-performance database, application server, caching and messaging functions into a single runtime instance, eliminating the need for separate technologies. By keeping data at the edge, Harper lets applications avoid the transit time of contacting a centralized database. Layers of resource-consuming logic, serialization, and network processes between each technology in the stack are removed, resulting in extremely low response times that translate into greater customer engagement, user satisfaction, and revenue growth.

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, which is essential in applications like recommendation systems, personalized content feeds, chatbot retrieval, image recognition, and natural language processing. The addition of vector indexing to the Harper platform eliminates the need for third-party vector databases – semantic caching can be done natively in Harper, helping bring down the overall costs of running AI models.

“There’s no question, AI is transforming the search box into an intent box,” said Stephen Goldberg, CEO and Cofounder of Harper. “Enabling semantic cache allows companies to do more than just deliver results, they can respond quickly with the right recommendations, products, and advice to improve customer satisfaction and drive conversion rates. Harper helps accelerate everything in your web experience, from contextual decision-making of AI to the consumer’s purchase journey overall.”

Harper is used by data architects and data teams at several Fortune 100 e-commerce companies and destination websites to yield massive savings for massive workloads. Other notable features found in the latest release of the performance platform include:

• New plugins API with support for dynamic loading
• HTTP logging for improved formatting, control and debugging
• New data loader for pre-loading content
• Resource API updates