Enabling GenAI in Market Intelligence

Market Intelligence at Machine Speed

Deliver GenAI-powered insights over massive proprietary datasets—without the performance bottlenecks or cost spikes of scaling RAG.

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Manager's Guide to RAG

Scaling GenAI-Powered Intelligence Without the Cost Spiral

Market Intelligence platform vendors face a unique challenge: delivering GenAI-powered insights over massive, domain-specific datasets while keeping response times low and infrastructure costs under control. Deep research and multi-step AI agents drive higher service value but also place enormous strain on computational resources. Vespa.ai was built for this scale—supporting high-throughput retrieval, multi-phase ranking, and real-time inference on billions of documents without runaway cost or complexity.

Deep Research for Competitive Advantage

Market Intelligence vendors have long used AI to collect, clean, and enrich vast, noisy data sources. Now, GenAI—delivered as domain-focused AI agents—is transforming how subscribers search, summarize, and act on market intelligence. Unlike consumer-grade tools trained on public web data, these platforms must blend curated business, financial, and product intelligence with subscriber-specific context to deliver precise, high-value insights. This deep integration unlocks richer intelligence but also pushes infrastructure to its limits.

The Challenge: Deep Research at Scale

Complex RAG pipelines for multi-step agent queries dramatically increase processing demands. When engineered well, they elevate service value; when poorly executed, they inflate costs, slow responses, and erode competitiveness. For this class of workload, performance, scalability, accuracy, and extensibility are critical—any compromise directly impacts service quality. Vespa.ai meets these demands, handling massive data volumes, intricate retrieval pipelines, and real-time multi-phase ranking at true production scale.

More than Vector Search

Vector databases alone don’t deliver scalable, production-grade RAG. While they handle nearest-neighbor search, real-world applications demand much more—combining semantic, keyword, and metadata retrieval, applying machine-learned ranking, and managing constantly changing structured and unstructured data. Scaling this across billions of documents with sub-100ms latency and thousands of concurrent queries forces you to stitch together multiple systems, introducing complexity, performance risks, and escalating infrastructure costs.

Vespa removes this burden by unifying vector search, hybrid retrieval, and real-time ranking in a single AI Search Platform, purpose-built to handle massive RAG workloads at production scale without the integration overhead.

RavenPack bigdata.com, a leader in data analytics for financial services, leverages Vespa for RAG to enable efficient search across millions of unstructured documents. RavenPack empowers clients to make informed decisions and capitalize on market opportunities by transforming extensive volumes of unstructured text into structured, actionable insights.

Why Vespa?

Accuracy

Real-time indexing keeps data fresh, ensuring answers are always current and reliable. With multi-phase ranking and custom ML models, Vespa delivers highly precise, context-aware results at any scale.

Low Latency

By co-locating compute, data, and models, Vespa minimizes network overhead and achieves sub-100ms response times—while keeping infrastructure and consumption costs under control.

Web Scale

Vespa has powered large-scale search for over a decade, combining keyword and vector retrieval as core features. Platforms like Perplexity, Yahoo, and Taboola handle 100,000+ queries per second across hundreds of billions of documents using Vespa.

Vespa For Market Intelligence Platforms

Deliver Generative Search. Provide a natural, chat-like experience that allows users to intuitively extract insights from hundreds of millions of premium documents in seconds.

Enterprise Intelligence. Securely power AI-driven search, summarization, and deep research on sensitive organizational knowledge—without sacrificing performance or control.

Scalable Performance. Handle billions of documents and thousands of concurrent queries with sub-100ms latency, keeping infrastructure costs predictable and manageable as demand grows.

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.

Other Resources

Enabling GenAI Enterprise Deployment with RAG

This guide outlines how businesses can deploy genAI effectively, using RAG to integrate private data for tailored, context-rich responses.

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The RAG Blueprint

Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.

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Delivering RAG for Perplexity

With Vespa RAG, Perplexity delivers accurate, near-real-time responses to more than 15 million monthly users and handles more than 100 million queries each week.