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E-commerce Customer Engagement

Power Personalized E-Commerce at Scale

Deliver fast, accurate, and personalized search and recommendations that keep customers engaged and boost revenue. Vespa.ai unifies search, personalization, and machine learning on a single platform designed for the scale of modern commerce.

Beyond Search: Personalization at Scale

E-commerce has entered a new age. Shoppers no longer settle for simple search results. They expect personalized, dynamic, and trustworthy experiences across every channel. Meeting these expectations is complex and resource-intensive, as businesses must balance personalization, system costs, innovation speed, and reliability at scale. Generative AI is shifting this balance by powering advanced search, visual discovery, and predictive personalization that anticipate customer needs in real-time. The result is a more engaging, responsive, and revenue-boosting customer experience.

The Answer is Vespa

That’s where Vespa comes in. Vespa.ai has been powering real-time search, recommendations, and personalization for some of the world’s largest E-commerce and digital media platforms long before vector search became a buzzword. Today, it’s a battle-tested platform used by global leaders across retail, media, finance, and beyond to solve the toughest challenges in personalization and discovery. With Vespa, businesses can engage customers with precise offers, drive deeper engagement to increase revenue, and stand out in a crowded market.

 

Powering E-commerce

The Vespa AI Search Platform powers E-commerce platforms by executing complex queries across structured data, unstructured text, and high-dimensional formats, such as vectors and tensors, which are crucial for advanced AI use cases, including personalization. It runs machine learning models directly within the ranking pipeline and supports multi-phase ranking to optimize relevance while controlling infrastructure costs. With built-in vector and keyword search support, scalable data serving, and continuous model updates, Vespa delivers fast, accurate results that drive better customer experiences and higher conversion rates.

Optimized over a decade, the platform’s architecture supports real-time processing of massive datasets with minimal latency without incurring network costs or performance delays from moving data. This enables instant query responses and dynamic updates based on user interactions, enhancing the shopping experience and business outcomes.

Smarter Product Discovery with Real-Time AI

See how Vespa uses tensors, semantic search, and real-time personalization to power intelligent, business-aware E-commerce experiences.

The Challenges Facing Modern E-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

E-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.

Use Cases Vespa Unlocks

From real-time personalization to multi-objective optimization, Vespa makes the once impossible possible. Complex use cases become straightforward to build, scale, and evolve on a single platform.

Vespa at Work

At Farfetch, Vespa powers the recommendation system, delivering personalized product suggestions to millions of users in real-time. The platform enables low-latency, high-throughput recommendations by efficiently handling large-scale data and integrating machine learning models directly into its serving layer.

Read the Farfetch blog

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 AI Search Platform Key Capabilities

  • Vespa provides all the building blocks of an AI search application, including vector database, hybrid search, retrieval augmented generation (RAG), natural language processing (NLP), machine learning, and support for large language models (LLM).

  • Vespa unifies search, recommendation, and personalization in one platform. This streamlined approach reduces complexity, accelerates development, and enables more cohesive, effective solutions—no more siloed thinking.

  • Build AI applications that meet your requirements precisely. Seamlessly integrate your operational systems and databases using Vespa’s APIs and SDKs, ensuring efficient integration without redundant data duplication.

  • Achieve precise, relevant results using Vespa’s hybrid search capabilities, which combine multiple data types—vectors, text, structured, and unstructured data. Machine learning algorithms rank and score results to ensure they meet user intent and maximize relevance.

  • Enhance relevance and personalization by leveraging Vespa’s real-time tensor operations. Go beyond keyword matching with support for vectors from text, images, location, and other complex data sources.

  • Enhance content analysis with NLP through advanced text retrieval, vector search with embeddings and integration with custom or pre-trained machine learning models. Vespa enables efficient semantic search, allowing users to match queries to documents based on meaning rather than just keywords.

  • Search and retrieve data using detailed contextual clues that combine images and text. By enhancing the cross-referencing of posts, images, and descriptions, Vespa makes retrieval more intelligent and visually intuitive, transforming search into a seamless, human-like experience.

  • Ensure seamless user experience and reduce management costs with Vespa Cloud. Applications dynamically adjust to fluctuating loads, optimizing performance and cost to eliminate the need for over-provisioning.

  • Deliver instant results through Vespa’s distributed architecture, efficient query processing, and advanced data management. With optimized low-latency query execution, real-time data updates, and sophisticated ranking algorithms, Vespa actions data with AI across the enterprise.

  • Deliver services without interruption with Vespa’s high availability and fault-tolerant architecture, which distributes data, queries, and machine learning models across multiple nodes.

  • Seamlessly handle increased demand with Vespa’s horizontal and vertical scaling capabilities, adding capacity on-demand to maintain peak performance during high-traffic periods.

  • Bring computation to the data distributed across multiple nodes. Vespa reduces network bandwidth costs, minimizes latency from data transfers, and ensures your AI applications comply with existing data residency and security policies. All internal communications between nodes are secured with mutual authentication and encryption, and data is further protected through encryption at rest.

  • Avoid catastrophic run-time costs with Vespa’s highly efficient and controlled resource consumption architecture. Pricing is transparent and usage-based.

Vespa E-commerce Stakeholders

Vespa adapts to your E-commerce 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 customer experiences. Vespa powers fast, accurate search, recommendation, and personalization at scale, helping customers find what they need and boosting engagement, conversions, and revenue. Its efficient architecture lowers costs and accelerates innovation for lasting competitive advantage.

Chief Revenue Officer (CRO) – Increase Average Order Value

With superior AI-driven search, recommendation, and personalization, Vespa drives higher conversion rates and enhances customer experience. Advanced search algorithms, personalized product recommendations, and tailored content allow customers to find what they are looking for quickly, discover new items they are likely to purchase, and enjoy a seamless shopping experience.

Chief Marketing Officer (CMO) – Increase Campaign Performance and Customer Satisfaction

Vespa enables real-time search, recommendation, and personalization at scale, ensuring every interaction is timely and tailored. This leads to higher engagement, increased conversion rates, and more effective campaign performance. With Vespa’s efficient infrastructure, your team can move faster, optimize spend, and continuously improve customer journeys.

eCommerce Manager – Optimize Search, Personalization and Recommendations at Speed

Vespa delivers high-performance search, recommendations, and personalization at scale—supporting fast response times even during peak traffic. Its efficient, scalable architecture enables consistent performance, reduces infrastructure costs, and simplifies operations, giving your team the flexibility to iterate quickly and deliver optimized shopping experiences that convert.

Technical Leaders

Vespa is a unified platform for powering search, recommendations, personalization, and product navigation at scale. It supports vector search, text relevance, structured data, ML ranking, tensor operations, real-time recommendations, grouping, aggregation, and faceting. With Vespa, teams avoid stitching together multiple tools and build on a modern, scalable platform proven in high-traffic, data-rich environments.

CTO/VP of Engineering – Build Advanced Personalized Search and Recommendation on a Single Platform

Deliver search, recommendations, and navigation from a single platform, enabling your AI teams to build hyper-personalized experiences that drive sales. With Vespa, teams move quickly from idea to production, whether experimenting with new ML models, adding signals, or refining business logic—all with the confidence that Vespa supports secure handling of user-generated content, fraud detection in ranking, and safe personalization. Built-in autoscaling helps manage cost and latency tradeoffs, ensuring performance at any scale.

Head of AI Engineering – Empower Your AI Team to Build Personalized, Scalable Experiences from Diverse Data

Build advanced services that integrate diverse data sources—from high-quality provider data to user-generated reviews, images, and ratings—supporting both extractive and generative features. Vespa facilitates concept-based navigation by letting your AI Team abstract from raw features to higher-level ideas like “city escape” or “family break,” enhancing discoverability and user experience. With built-in support for personalization, Vespa makes it easy to tailor recommendations and experiences to individual user preferences, powering AI-driven applications at scale.

 

AI Team

Develop applications that leverage data and signals to deliver the performance and quality demanded of modern e-commerce solutions without being limited and encumbered by integrating different technologies for vectors, text, signals, and ranking.

Search Engineer – Power Relevant, Real-Time Search with Geo, Personalization, and Trust Signals

Build smarter, more responsive search and recommendation experiences by combining real-time updates, geographic relevance, and personalized ranking. Vespa’s strong geo-filtering capabilities ensure users see products, offers, and content relevant to their location, while its support for real-time data keeps search results aligned with current inventory—showing only what’s truly available. Engineers can also incorporate user-generated content, ratings, and reputation signals to boost trustworthy results and detect fraudulent behavior. With built-in support for personalized ranking, Vespa enables precise, context-aware search at scale.

Read more.

Data Scientist – Deliver Smarter Product Rankings with Real-Time, Scalable ML Evaluation

Deliver highly relevant product results by enabling real-time evaluation of ML models on every query. Vespa supports deep, low-latency scoring across large product catalogs using live signals like user behavior, product metadata, vector embeddings, and text relevance. With robust tensor computation, Vespa lets you implement cutting-edge models, and by running your models and computations directly on content partitions, Vespa eliminates the need to move data—making it possible to scale personalized ranking and search quality efficiently.

Read more.

AI Engineer – Power Accurate, Scalable Retrieval for RAG and Beyond

Build accurate, scalable RAG systems and intelligent applications with full control over retrieval, ranking, and delivery. Vespa’s unified platform combines advanced retrieval techniques—including vector search, keyword relevance, and structured filtering—with multi-signal scoring from embeddings, metadata, and behavioral data. Vespa supports multi-vector models like ColBERT, runs ONNX models at query time, and offers fully customizable ranking logic. Designed for production scale, Vespa handles billions of documents and high query volumes with low latency and reliability.

Read more.

 

Ready to Unlock Better E-Commerce Experiences?

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

More Reading

ESG Research Report: How Generative AI is Changing E-Commerce

Unlock the Future of E-commerce Webinar – One Platform, Unlimited Possibilities

Enabling GenAI Enterprise Deployment with RAG

Crafting a Sophisticated Retail AI Assistant with Vespa and LangGraph

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