Harper Now Features Vector Indexing for AI-Powered Search
Release 4.6 Supports Vector-Based Data for Semantic Caching and Semantic Search – EnablingUsers to Better Understand Intent, Deliver Relevant Results, and Drive Conversion Rates
DENVER, June 30, 2025 /PRNewswire/ — Harper, bringing next-level web performance to a digital-first world, today 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'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
To learn more about these new features visit the Harper v. 4.6 release notes here.
About HarperHarper enables companies to achieve web speed, scale, and performance levels never seen before – with customers reporting as much as 7x faster page loads, nearly 30x faster LCPs, and more than 25 percent year-over-year revenue growth. Its innovative, backend technology collapses the traditional software stack into one highly-efficient, low-latency system with limitless horizontal scale. By combining data, application, cache, and messaging functions into a single process, on a single server, Harper eliminates the constraints and complexity of building, distributing, and maintaining data-dependent applications. In doing so, Harper unlocks new revenue streams for its clients and inspires dev teams to innovate without compromise. To learn more, visit www.harpersystems.dev
Media Contact:April BurghardtPR & Communicationsapril@harperdb.io646-246-0484
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GPCR-Targeting Therapies Epidemiology Segmentation The GPCR-targeting therapies market report proffers epidemiological analysis for the study period 2020–2034 in the 7MM, segmented into: Total Cases in Selected Indications for GPCR-Targeting Therapies Total Eligible Patient Pool in Selected Indications for GPCR-Targeting Therapies Total Treated Cases in Selected Indications for GPCR-Targeting Therapies GPCR-Targeting Therapies Report Metrics Details Study Period 2020–2034 GPCR-Targeting Therapies Report Coverage 7MM [The United States, the EU-4 (Germany, France, Italy, and Spain), the United Kingdom, and Japan] Key Indications Covered in the Report PTCL, CTCL, Migraine, NASH, and others Key GPCR-Targeting Therapies Companies CytoDyn, REMD Biotherapeutics, GPCR Therapeutics, Structure Therapeutics, Pfizer, Kyowa Hakko Kirin, Novartis, Amgen, Lundbeck, and others Key GPCR-Targeting Therapies Leronlimab, Volagidemab, GPC-100, GSBR-1290, ZAVZPRET, POTELIGEO, AIMOVIG, VYEPTI, and others Scope of the GPCR-Targeting Therapies Market Report GPCR-Targeting Therapies Therapeutic Assessment: GPCR-Targeting Therapies current marketed and emerging therapies GPCR-Targeting Therapies Market Dynamics: Conjoint Analysis of Emerging GPCR-Targeting Therapies Drugs Competitive Intelligence Analysis: SWOT analysis and Market entry strategies Unmet Needs, KOL's views, Analyst's views, GPCR-Targeting Therapies Market Access and Reimbursement Discover more about GPCR-targeting therapies in development @ GPCR-Targeting Therapies Clinical Trials Table of Contents 1. Key Insights 2. Report Introduction 3. Executive Summary 4. Key Events 5. Epidemiology and Market Forecast Methodology 6. GPCR-Targeting Therapies Market Overview at a Glance in the 7MM 6.1. Market Share (%) Distribution by Indications in 2020 6.2. Market Share (%) Distribution by Indications in 2034 7. GPCR-Targeting Therapies: Background and Overview 7.1. Introduction 7.2. Potential of GPCR-Targeting Therapies in Different Indications 7.3. Clinical Applications of GPCR-Targeting Therapies 8. Treatment and Management 9. Target Patient Pool of GPCR-Targeting Therapies 9.1. Assumptions and Rationale 9.2. Key Findings 9.3. Total Cases of Selected Indication for GPCR-Targeting Therapies in the 7MM 9.4. Total Eligible Patient Pool of Selected Indication for GPCR-Targeting Therapies in the 7MM 9.5. Total Treatable Cases in Selected Indication for GPCR-Targeting Therapies in the 7MM 10. Marketed Therapies 10.1. Key Competitors 10.2. OTELIGEO (mogamulizumab-kpkc): Kyowa Hakko Kirin 10.2.1. Product Description 10.2.2. Regulatory milestones 10.2.3. Other developmental activities 10.2.4. Clinical development 10.2.5. Safety and efficacy 10.3. AIMOVIG (erenumab-aooe): Novartis/Amgen 10.3.1. Product Description 10.3.2. Regulatory milestones 10.3.3. Other developmental activities 10.3.4. Clinical development 10.3.5. Safety and efficacy List to be continued in the report 11. Emerging Therapies 11.1. Key Competitors 11.2. Volagidemab: REMD Biotherapeutics 11.2.1. Product Description 11.2.2. Other developmental activities 11.2.3. Clinical development 11.2.4. Safety and efficacy 11.3. Leronlimab: CytoDyn 11.3.1. Product Description 11.3.2. Other developmental activities 11.3.3. Clinical development 11.3.4. Safety and efficacy 12. GPCR-Targeting Therapies: Seven Major Market Analysis 12.1. Key Findings 12.2. Market Outlook 12.3. Conjoint Analysis 12.4. Key Market Forecast Assumptions 12.4.1. Cost Assumptions and Rebates 12.4.2. Pricing Trends 12.4.3. Analogue Assessment 12.4.4. Launch Year and Therapy Uptakes 12.5. Total Market Sizes of GPCR-Targeting Therapies by Indications in the 7MM 12.6. The United States Market Size 12.6.1. Market Size by Indication in the United States 12.6.2. Market Size by Therapies in the United States 12.7. EU4 and the UK 12.7.1. Market Size by Indications in EU4 and the UK 12.7.2. Market Size by Therapies in EU4 and the UK 12.8. Japan 12.8.1. Market Size by Indications in Japan 12.8.2. Market Size by Therapies in Japan 13. KOL Views 14. Unmet Needs 15. Market Access and Reimbursement 16. Appendix 16.1. Bibliography 16.2. Report Methodology 17. DelveInsight Capabilities 18. Disclaimer 19. 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