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Retrieval-Augmented Generation for the Enterprise

Not all RAG methods are created equal

Vespa drives relevant, accurate, and real-time answers from all of your data, with unbeatable performance.

RAG at Machine Speed: Built for the Demands of Deep Research

Retrieval-Augmented Generation (RAG) is becoming foundational for enterprises adopting generative AI. By grounding large language models (LLMs) in private, structured, and unstructured data, RAG enables secure, context-aware responses tailored to real business needs to power customer-facing applications like conversational support, intelligent search, and personalized self-service, as well as internal tools for research and decision automation.

But the demands on retrieval are changing. We are entering an era of deep research, where AI agents must perform multi-step reasoning, issuing many retrievals in sequence to explore, verify, and synthesize information. This shift requires retrieval systems that operate at machine speed and scale. Organizations still relying on systems optimized for human-speed analysis will struggle to keep up—and risk falling behind.

From Prototype to Production: RAG That Scales with Your Business

Proving the value of RAG in a prototype is one thing; scaling it across the enterprise is another. Organizations face significant challenges, including consolidating diverse data sources, ensuring privacy and access control, meeting low-latency performance targets, and operating a complex real-time inference pipeline at scale. As RAG expands to support more users, domains, and deep research, the demands on infrastructure skyrocket.

That’s why Perplexity chose Vespa.ai—the only production-proven platform capable of delivering real-time, large-scale RAG with the performance and reliability their users expect. Vespa combines vector search, structured filtering, semantic retrieval, and machine-learned ranking in a single engine, eliminating the need to stitch together multiple systems.

Perplexity’s success demonstrates Vespa’s ability to power high-throughput RAG in demanding, customer-facing environments. Whether you’re launching internal copilots, AI-powered assistants, or vertical search applications, Vespa gives you the infrastructure to scale confidently—from prototype to full production.

Speed, at Scale

As your business expands, so do your data and user demands. Other platforms compromise speed to handle growing workloads.

Vespa delivers fast performance by distributing data and computation across a cluster of nodes. Processing queries and applying ML models close to the data minimizes network delays and scales effortlessly, ideal for real-time AI and search at a massive scale.

Vespa delivers seamless horizontal and vertical scaling, allowing your applications to process big data efficiently. By automatically adjusting capacity based on demand, Vespa Cloud optimizes resource usage and keeps costs in check.

With Vespa, you deliver lightning-fast responses, no matter how much your business grows.

Read more about Vespa scaling.

Unmatched Accuracy, Beyond Vectors

While vector databases are essential for generative AI, they only form part of the puzzle. Achieving relevant, actionable insights requires a powerful hybrid search approach that combines vector search with traditional methods, all powered by machine learning.

Vespa goes beyond just vectors. As a comprehensive search engine and vector database, it integrates multiple retrieval techniques, including vector search, lexical search, and hybrid search, to enhance relevance and precision. Its advanced indexing and ranking systems ensure you retrieve the most accurate data, every time.

With Vespa, you can ensure accuracy with every query, boosting your decision-making with precise, timely information.

Read more about Vespa enterprise search.

Any ML Model, Any Time

Innovate with cutting-edge ML models while maintaining full control. Vespa seamlessly supports a wide range of retrieval, ranking, and inference methods out of the box, elevating your application’s capabilities.

Vespa’s inherently flexible platform lets you express any computation using tensor math across a combination of text, metadata, and vector/tensor fields. This versatility makes it easy to implement and customize various models and methods on the platform. 

With Vespa, you stay ahead of the curve, constantly leveraging the latest in ML technology.

Learn more about Vespa model integration.

More Intelligent Ranking, Lower Cost

To deliver fast, relevant results without increasing infrastructure costs, Vespa supports multi-phase ranking, which provides a staged approach to search and retrieval. Vespa first retrieves a broad set of candidates using efficient keyword and vector search. It then applies increasingly sophisticated ranking models, starting with lightweight filters and progressing to deeper ranking models (including tensor-based ML inference), only on the most promising results. This ensures users see the most relevant answers while keeping runtime costs under control.

Read the blog: Introducing layered ranking for RAG applications.

Built-in Security, at the Core

Security is at the core of Vespa’s architecture. From encryption and key management to secure storage and network protection, Vespa shields your data from unauthorized access, misuse, and corruption.

Comply with industry standards like GDPR, HIPAA, and more with ease. Vespa provides the tools and guidance necessary to ensure your RAG systems meet regulatory requirements, safeguarding user privacy and sensitive data at every turn.

With Vespa, you can trust that your data and your users are always secure.

Read the Vespa Cloud Security White Paper.

Billion-Scale PDF Search

Vespa uses visual models such as ColPali to simplify and enhance information retrieval from complex, visually rich documents, including PDFs. ColPali enhances document retrieval by embedding entire rendered documents, including visual elements, into vector representations optimized for Large Language Models (LLMs). By treating documents as visual entities rather than text, ColPali eliminates complex preprocessing, preserves visual context, and streamlines the RAG pipeline.

Because Vespa brings computation to data distributed across many nodes, reducing network bandwidth costs and latency, Vespa is well suited to billion-scale PDF applications.

Read about Vespa support for vision language models.

Vespa AI Search Platform Key Capabilities

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

  • 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 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.

  • 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 RAG Stakeholders

Vespa adapts to your organization’s needs, benefiting business leaders, technical leaders, and AI teams. Executives gain from AI automation, while AI teams build and manage cutting edge scalable AI applications.

Business Leaders

Enhance customer interactions, streamline operations, and drive strategic planning with generative AI powered by your corporate data. Vespa RAG ensures AI delivers relevant, up-to-date insights, allowing business leaders to focus on results, not technology.

Top RAG/Generative AI Use Cases by Business Leader

CMO (Chief Marketing Officer)

  • Personalized Content Generation – Create targeted marketing copy, blog posts, and ads.
  • Customer Sentiment Analysis – Extract insights from reviews and social media.
  • AI-Powered Chatbots & Engagement – Automate customer interactions with natural responses.
  • Campaign Performance Insights – Analyze marketing data for optimization.

COO (Chief Operating Officer)

  • Intelligent Document Processing – Automate data extraction from reports, invoices, and contracts.
  • Supply Chain Optimization – Enhance forecasting and logistics with real-time insights.
  • Operational Efficiency Automation – Streamline workflows with AI-driven process improvements.
  • Vendor & Contract Analysis – Improve decision-making with AI-assisted contract reviews.

CHRO (Chief Human Resources Officer)

  • HR & Employee Self-Service – Enable AI-driven knowledge assistants for HR queries.
  • Talent Acquisition & Screening – Automate resume analysis and candidate matching.
  • Workforce Sentiment Analysis – Gauge employee satisfaction from surveys and feedback.
  • Learning & Development Personalization – Tailor training programs with AI-driven recommendations.

Chief Risk & Compliance Officer

  • Regulatory & Compliance Assistance – Ensure adherence to evolving regulations with AI-driven insights.
  • Fraud Detection & Risk Analysis – Identify anomalies and detect suspicious patterns.
  • Contract & Policy Compliance Review – Automate legal and regulatory document analysis.
  • Cybersecurity & Threat Intelligence – Enhance risk monitoring with AI-driven alerts.

Technical Leaders

Enhance AI accuracy, scalability, and efficiency with RAG. Vespa’s cost-effective infrastructure and flexible platform seamlessly integrate with existing data systems, enabling enterprise-wide deployment across diverse applications.
CTO / VP of Engineering

Enterprise-Scale RAG That Evolves With Your Business

Scaling RAG for the enterprise demands a platform that maintains quality as data, use cases, and complexity grow. Vespa supports all data types, enables large-scale deployment, and runs any retrieval and ranking strategy. Proven in enterprise environments, it handles diverse use cases with billions of data points and high query volumes. With decades of AI and data expertise, Vespa is the trusted choice for building scalable, future-proof AI solutions.

Head of AI / AI Architect

Scale RAG Across All Your Data with Confidence

Building enterprise-grade RAG applications requires a platform that scales with your needs. Vespa seamlessly integrates vectors, text, and structured data, applying machine-learned models and tensor computations for precise results. With automated data distribution, real-time indexing, and distributed inference, it ensures efficiency at any scale. Proven across diverse use cases with billions of data points and high query volumes, Vespa is the trusted choice for enterprise AI success.

AI Team

Vespa RAG offers high performance, flexibility, and efficiency with low-latency retrieval, high query throughput, and scalable architecture for large datasets. It integrates with ML pipelines, supports fine-tuned models, and provides efficient indexing, hybrid search, and robust APIs for seamless deployment and experimentation.

Search Engineer – Apply Advanced IR for RAG at Scale
Vespa combines a proven text search engine with industry-leading support for vectors, tensors, and machine learning. Its flexible ranking framework integrates text matching, vector similarity, and other signals using any mathematical function, including machine-learned models. This enables advanced information retrieval techniques to tackle RAG challenges at any scale, from small datasets to enterprise-level demands.

Read more.

AI Engineer – Guarantee RAG Accuracy with Advanced Data Retrieval
A RAG application is only as effective as the data it retrieves. Beyond vector similarity, advanced techniques like hybrid search, multi-signal integration, and machine-learned ranking are essential. Vespa seamlessly combines these methods with a proven distributed architecture that scales effortlessly to handle any data size or traffic volume, ensuring high performance and flexibility for AI applications.

Read more.

Data Scientists – Unlock Advanced Retrieval and Ranking Models
Vespa enables vector and hybrid retrieval using all data signals, including tensors, full-text, and metadata. It supports distributed tensor ranking and seamless integration of machine-learned models. With powerful tensor computation, Vespa lets you implement cutting-edge models today while staying ready for future innovations.

Read more.

Vespa at Work

“RavenPack has trusted Vespa.ai open source for over five years–no other RAG platform performs at the scale we need to support our users. Following rapid business expansion, we transitioned to Vespa Cloud. This simplifies our infrastructure and gives us access to expert guidance from Vespa engineers on billion-scale vector deployment. This move allows us to concentrate on delivering innovative solutions to meet our users’ increasingly sophisticated demands.”

“We chose Vespa because of its richness of features, the amazing team behind it, and their commitment to staying up to date on every innovation in the search and NLP space. We look forward to the exciting features that the Vespa team is building and are excited to finalize our own migration to Vespa Cloud.”

Yuhong Sun,  Co Founder/CoCEO

Perplexity.ai leverages Vespa Cloud as its web search backend, utilizing a hybrid approach that combines multi-vector and text search. Vespa supports advanced multi-phase ranking, ensuring more accurate and relevant search results.

Ready to Unlock the Power of Generative AI?

Generative AI only delivers real business value when it’s built on the right foundation. Vespa.ai is the world’s first AI Search Platform, unifying vector, keyword, and structured retrieval with machine-learned ranking and real-time inference. Trusted by leaders like Perplexity, Spotify, and Yahoo, Vespa delivers the speed, scale, and accuracy required for deep research, agentic AI, and customer-facing generative applications.

Explore More

Retrieval Augmented Generation

Discover Vespa’s RAG features for hybrid search, combining text-vector, token-vector, and machine-learned ranking, all designed to scale effortlessly and handle any query volume or data size without compromising on quality.

BARC Research Paper

To help organizations navigate their choice in RAG adoption, BARC has prepared the research note: Why and How Retrieval-Augmented Generation Improves GenAI Outcomes. Download your free copy here.

Vespa RAG Manager’s Guide

This management guide outlines how businesses can deploy generative AI effectively, focusing on Retrieval-Augmented Generation (RAG) to integrate private data for tailored, context-rich responses.

RAG Technical Guide

Learn how Vespa RAG allows language models to access up-to-date or specific domain knowledge beyond their training, improving performance in tasks such as question answering and dynamic content creation.