Search & Product Discovery for Digital Commerce

Personalized Relevance and Product Discovery at Scale

Deliver hybrid keyword and vector search, personalized ranking, algorithmic merchandising, catalog navigation, analytics, and guided selling across the entire digital commerce journey: From first query to checkout.

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Beyond Search: Personalized Discovery at Scale

Digital commerce search and recommendation have evolved beyond basic keyword matching and globally ranked product lists. Personalized relevance is now the minimum expectation informed by real-time behavioral signals and session context. Customers expect search and product discovery experiences that adapt dynamically across search results, category pages and navigation rather than static rankings driven solely by periodically computed popularity signals.

At the same time, digital commerce teams must balance personalization, merchandising control, infrastructure efficiency, and continuous optimization. The rise of generative AI and semantic retrieval adds further complexity, requiring platforms that can combine structured catalog data, behavioral signals, and unstructured data using embeddings and machine-learned models in real time.

Vespa addresses these challenges with a unified platform for scalable search, personalization, and product discovery.

Powering Search & Product Discovery

Vespa provides hybrid keyword and vector search, combining precise lexical matching with semantic intent understanding. It supports advanced semantic search using natural language processing, embeddings, and structured product attributes within a single serving layer.

The platform executes machine-learned ranking models directly in the retrieval pipeline and supports multi-phase ranking strategies to optimize relevance while controlling infrastructure costs. Structured data, unstructured content, and high-dimensional vector and tensor representations are indexed and ranked together, enabling consistent discovery experiences across search and browse.

This architecture enables fast, high-quality search and recommendation, continuous ranking optimization, and real-time adaptation to personal behavioral signals.

Watch the product discovery demo

Personalized Relevance and Optimization

Personalized relevance is driven by real-time behavioral data, session context, and aggregate customer interactions. Vespa enables dynamic ranking updates based on clickstream signals, conversion data, and business objectives.

Search and product listing pages can be continuously optimized through configurable ranking logic and experimentation frameworks, supporting A/B testing and iterative relevance improvements without service disruption.

This allows digital commerce teams to balance algorithmic performance with business strategy and merchandising priorities.

Algorithmic Merchandising

Modern digital commerce requires more than static rule-based boosting. Merchandising must adapt dynamically to inventory levels, campaign objectives, conversion signals, and sponsored placements.

Vespa provides a programmable merchandising framework that supports rule-based curation, automated ranking adjustments, and algorithmic optimization within the same ranking pipeline. Business rules, promotional boosts, inventory-aware ranking, and sponsored product placements can be applied alongside machine-learned relevance models.

This enables retailers and marketplaces to combine curated control with data-driven merchandising automation, reducing manual effort while maintaining strategic oversight.

Catalog Enrichment and Unified Product Data

Poor or inconsistent catalog data remains a major obstacle to effective search and product discovery, particularly in large or B2B assortments.

Vespa enables real-time product catalog enrichment by combining structured attributes, behavioral signals, semantic embeddings, and external data sources within a unified index. This allows product content, inventory, and behavioral patterns to inform relevance ranking and semantic retrieval without requiring separate data pipelines.

By serving enriched and normalized product data directly within the search layer, Vespa improves retrieval quality, supports semantic matching, and enables more descriptive content for generative AI and answer engine consumption.

Both B2C and B2B Digital Commerce

Modern Search & Product Discovery must support both consumer retail and complex B2B environments. B2B commerce often involves large, attribute-rich catalogs, contract pricing, buyer-specific assortments, and technical documentation that must be searchable alongside products.

Vespa unifies structured attributes, descriptive content, behavioral signals, and semantic embeddings in a single retrieval and ranking layer. This enables accurate discovery across both consumer marketplaces and complex B2B catalogs, without separate search systems or rigid taxonomies.

AI Quick Fix Webinar Series

The Practical Path to AI for Digital Commerce

A series of short, practical sessions addressing the core challenges in digital commerce search and personalization — from zero-result queries to weak relevance and fragmented data. Each session tackles one fixable problem and shows how targeted AI capabilities can deliver measurable gains in engagement and revenue. Vespa.ai is used as the reference platform for real-world examples, but the content is broadly educational and applicable to any stack.

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The Challenges Facing Modern Digital Commerce

Personalization at Scale

Every shopper expects a tailored experience. Preferences shift by user, location, season, or even mood. True personalization must be both real-time and contextual, adapting instantly to deliver relevance and engagement at every touchpoint.

Business Objectives in Motion

Digital commerce must constantly balance competing goals, including localization, vendor strategies, stock levels, seasonal shifts, and margins. As markets change dynamically, strategies must adapt in real-time, ensuring every interaction advances both business goals and shopper experience.

Innovation Speed

Innovation cycles have collapsed. What once took months must now happen in days or hours. Teams need to experiment continuously, rolling out, testing, and scaling new features rapidly, without bottlenecks from fragmented systems or complex integrations.

Collaboration Across Teams

Search, recommendation, ads, and data science teams all depend on each other. Collaboration should be seamless, with new features configured, not rebuilt, so experimentation is safe, fast, and efficient. Evolution should accelerate without costly rewrites or silos.

Real-Time Measurement and Control

Personalization must be guided by live user behavior. Metrics should feed directly into business logic and decision-making without delay. Without real-time feedback, systems lag, missing opportunities to engage and optimize at the moment.

The Cost–Latency Paradox

Every millisecond matters, but distributed systems waste time and money shuffling data across services. Businesses need to keep data close to where it’s used to cut latency, boost performance, and control cost without sacrificing functionality.

Why Tensors Matter for Modern Search

Hybrid keyword and vector search are now table stakes in digital commerce. But modern product discovery requires combining multiple complex signals in real time, including product semantics and behavioral understanding. Vespa’s native tensor engine makes this possible.

Learn how tensors power advanced ranking and personalization

Vespa at Work

Vinted transitioned to Vespa to enhance the performance and scalability of their search and recommendation systems. Vespa’s ability to handle complex, large-scale data and support machine learning models natively allowed Vinted to provide faster, more personalized search results.

Read the Vinted Blog

Otto.de enhances autosuggestions with Vespa, significantly improving relevance, performance, and scalability. With Vespa’s machine learning capabilities and flexible deployment, Otto.de personalizes suggestions, reduces latency, and delivers a superior user experience.

Vespa Digital Commerce Stakeholders

Vespa adapts to your needs helping business leaders drive AI-powered growth while enabling AI teams to build and manage search, recommendation, and personalization at scale.

Business Leaders

Drive growth through better digital commerce experiences. Vespa powers search, personalized relevance, merchandising, and product discovery at scale — helping customers find what they need, increasing engagement and conversion, and enabling continuous optimization with efficient infrastructure.

Chief Revenue Officer (CRO) – Increase Revenue and Conversion

Vespa improves conversion, average order value, and revenue through personalized search and product discovery. Hybrid keyword and semantic retrieval, real-time behavioral ranking, and optimized recommendations ensure customers quickly find relevant products, reducing abandonment and increasing completed transactions.

Chief Marketing Officer (CMO) – Increase Campaign Performance and Engagement

Vespa enables dynamic merchandising and personalized discovery aligned to campaign objectives. Promote priority products, adjust ranking in real time, and measure impact through continuous optimization to ensure marketing investments translate into measurable engagement, conversion lift, and stronger customer satisfaction.

eCommerce Manager – Optimize Search and Merchandising Performance

Vespa provides control over search, ranking strategies, and product listing experiences. Balance personalization with business rules, test new approaches, and adapt quickly to seasonal demand, inventory changes, and performance signals without compromising speed or operational efficiency.

Merchandiser – Combine Curated Control with Algorithmic Ranking

Vespa enables merchandisers to apply business rules, promotional boosts, and sponsored placements alongside algorithmic relevance models. Maintain control over product visibility while leveraging behavioral data and automation to improve discovery and reduce manual merchandising effort.

Category Manager – Optimize Category and Product Listing Page Performance

Vespa helps category managers improve product listing pages (PLP) using real-time performance insights and configurable ranking logic. Adjust assortment visibility, respond to demand trends, and refine navigation to ensure categories drive both effective discovery and measurable commercial results.

Technical Leaders

Vespa is a unified Search & Product Discovery platform for digital commerce. It combines hybrid retrieval, machine-learned ranking, merchandising logic, and large-scale data serving in a single runtime, reducing architectural complexity while delivering predictable performance and cost control.

CTO/VP of Engineering – Consolidate and Scale Search & Product Discovery

Vespa replaces fragmented search, recommendation, and ranking services with a unified platform. Execute models in-line, support hybrid retrieval, and scale under peak traffic. This simplified architecture delivers low latency, operational control, and infrastructure efficiency.

Head of AI Engineering – Deploy and Iterate on Production-Grade Ranking Models

Vespa enables AI teams to serve embeddings, behavioral signals, and structured attributes within multi-phase ranking pipelines. Test, iterate, and deploy models directly in the serving layer, combining lexical and semantic relevance without separate inference systems.

Search Manager – Control Hybrid Retrieval and Relevance Tuning

Vespa provides fine-grained control over lexical and semantic ranking, faceting, filtering, and query intent logic. Analyze performance, tune blending strategies, and optimize search and navigation across large, attribute-rich catalogs.

AI Team

Build intelligent Search & Product Discovery systems without stitching together separate vector, keyword, ranking, and inference services. Vespa unifies retrieval, machine-learned ranking, personalization signals, and large-scale data serving in a single production-grade platform.

Search Engineer – Deliver Hybrid, Real-Time Retrieval at Scale

Implement hybrid keyword and vector search with control over filtering, faceting, geo-relevance, and trust signals. Incorporate real-time inventory updates and behavioral data into ranking pipelines, ensuring accurate, low-latency search across dynamic, large-scale catalogs.

Data Scientist – Deploy and Optimize Machine-Learned Ranking

Serve embeddings, structured attributes, and behavioral signals within multi-phase ranking pipelines. Evaluate and iterate on models in real time, leveraging tensor operations and in-line inference to improve relevance without external model-serving systems.

AI Engineer – Build Production-Grade Retrieval for RAG and Generative Interfaces

Design scalable retrieval systems combining vector search, lexical relevance, structured filtering, and multi-signal ranking. Serve machine-learned models at query time, support multi-vector approaches such as ColBERT, and deliver low-latency retrieval for conversational and generative AI applications.

Ready to Unlock Better Digital Commerce Experiences?

Vespa powers search, personalization, and recommendations for some of the world’s largest digital commerce and media platforms. Start building today and see how Vespa helps you deliver relevance, reliability, and revenue at scale.

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