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AI Search Platform

The Search Platform That Powers AI

Delivering RAG at scale, with rapid, accurate results, is as challenging as it is transformative. An AI Search Platform unites classical search with AI to power agents, applications, and user queries with fast, precise results over billions of documents and massive, ever-changing datasets.

The Backbone of AI: Why Retrieval Matters More Than Ever

Search was built for human speed: deliver a shortlist of ranked ordered results, let the user scan, interpret, and decide the best fit. AI operates at machine speed, enabling multi-step retrievals, vast context windows, and delivering answers in natural language, but without relying on human guidance for results. Users expect GenAI not just to return accurate results, but to do the heavy-lifting: answer a question, summarize research, or even solve a problem.

That shift demands a new foundation to deliver better answers, lower latency, and controlled costs at production scale.

GenAI Maturity Levels

Level 1

Conversational Q&A

“Answer my question”

Level 2

Deep Research

“Research this and report back”

Level 3

Agentic Systems

“Solve my problem”
Each step in GenAI maturity increases pressure on the retrieval layer for speed, scale, and accuracy.

The Challenge: Delivering Retrieval Accuracy at Scale

Vector databases made similarity search possible, allowing LLMs to ground their answers in vast unstructured datasets. But vector search alone isn’t enough. Production-grade AI search must also combine semantic, keyword, and metadata retrieval, apply machine-learned ranking, and manage constantly changing structured and unstructured data, all at scale, while keeping consumption costs under control.

When these capabilities are bolted together across separate systems, the cracks show quickly. Bandwidth limits, integration overhead, and shallow connections turn into bottlenecks and undermine accuracy, a critical weakness when users tend to trust AI outputs without question.

What is an AI Search Platform?

The AI Search Platform is a new class of infrastructure that makes retrieval smarter, faster, and more scalable by uniting classical search techniques with modern AI: vector and tensor search in embedding spaces, full-text search for precision, multi-step ranking and real-time inference, using machine-learned models and tensor math. It enables accurate search at machine speed with filtering and ranking to ensure only the most relevant answers surface instantly. The AI Search Platform is critical in simplifying the development and deployment of generative AI at every maturity level.

Enterprise Search Platforms vs AI Search Platform

Employee-Facing: Enterprise Search Platforms

Designed to improve productivity by helping staff find information across digital workplace tools, intranets, service hubs, compliance, and IT knowledge bases. They emphasize ease of adoption and provide enterprise-readiness features out of the box, including governance frameworks, prebuilt connectors, permission-aware search, and other packaged capabilities. In some instances, these can also be used in simple customer-facing scenarios such as service portals or basic product discovery. Examples include Coveo Relevance Cloud, Elasticsearch, Glean, and Google Vertex AI Search.

Customer-Facing: AI Search Platforms

Power mission-critical applications such as search, recommendation, personalization, and RAG at web scale. Here, performance, scale, and accuracy are existential because end-user experience and revenue depend directly on them. These platforms are infrastructure-oriented, prioritizing low latency, hybrid search, extensibility, and advanced ranking (including tensor support). Designed for complex data and multimodal content, where precise retrieval and real-time computation are essential. Typically require engineering expertise to operate at scale. Vespa.ai is purpose-built for this category.

What About Data Platforms?

Mainstream data platforms, such as Snowflake and Postgres, now offer basic vector search capabilities. These features are “good enough” for entry-level GenAI chatbots and simple internal tasks, with the added benefit of working directly on centralized data. For customer-facing deep research or agentic AI, accuracy, scale, and speed are non-negotiable. Or where data resides in PDFs and other unstructured sources outside the warehouse, an AI Search Platform is essential.

For CIOs, this has created a clear split:

  • Basic enterprise GenAI: handled by incumbent platforms, suitable for simple, internal use.
  • Advanced enterprise GenAI: demanding, customer-facing use cases where only AI Search Platforms can keep pace.

Enterprises that embrace AI Search Platforms for these high-stakes use cases will set the pace in this new era. Search is no longer just a utility—it’s becoming the backbone of AI-driven business.

 

Vector Databases vs. Data Warehouses vs. AI Search Platforms

This table compares the three main approaches to delivering retrieval: vector databases, data warehouses with vector support, and AI Search Platforms. It highlights how their capabilities differ, and why only a full AI Search Platform can meet the performance, scale, and accuracy demands of production-grade generative AI.

AI Search Platforms vs Vector Database vs Data Warehouses

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 powers search, personalization, and recommendation, and delivers the speed, scale, and accuracy required for deep research, agentic AI, and customer-facing generative applications.

Other Resources

Vespa AI Search Platform in 90 seconds

Get a high-level introduction to Vespa.ai. In just 90 seconds, you’ll understand how Vespa is positioned as an AI Search Platform built for performance, scalability, and accuracy—core requirements for powering modern AI-driven applications. Ideal for a quick orientation to what sets Vespa apart.

BARC Research Report

This research note explores the emergence of versatile AI databases that support multi-model applications. Practitioners, data/AI leaders, and business leaders should read this report to understand this new platform option for supporting modern AI/ML initiatives.

Enabling GenAI Enterprise Deployment with with RAG

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.