Information Platforms

Built for:

Business Information • Competitive Intelligence • Financial Intelligence • Legal Research • Market Intelligence • Scientific Publishing

Although they serve different markets, these organizations share many of the same AI retrieval and engineering challenges—and many of the same architectural solutions.

industry trend

Information Platform Providers Are Becoming AI Companies

Organizations that build information platforms, whether competitive intelligence, financial data, legal research, scientific publishing, or business information services, are undergoing a fundamental transformation. Users no longer want to search vast collections of proprietary content themselves; they expect AI to investigate, summarize, reason, and deliver insights directly into the workflows where decisions are made.

Industry leaders such as AlphaSense have recognized this shift early, combining proprietary business data with AI-powered search to redefine how professionals consume market intelligence. Gartner recently recognized AlphaSense as a Leader in the Magic Quadrant for Competitive and Market Intelligence Platforms, reflecting the growing demand for AI-native information platforms.

Public AI assistants have accelerated user expectations, but they also highlight an important limitation: they are primarily trained on publicly available information. The next generation of AI applications will increasingly differentiate themselves through proprietary data, premium content, and domain expertise, combining trusted internal and external information to produce accurate, explainable insights. For information platform providers, AI is no longer simply another feature—it is becoming the primary way customers discover, consume, and act on information.

challenge

The New AI Challenge

AI is changing how customers interact with proprietary information. Delivering these new experiences introduces architectural challenges that many existing search stacks weren't designed to handle.

AI agents increase workload

AI agents don't search once. They repeatedly retrieve, rank, filter, verify, and synthesize information, increasing search workloads by orders of magnitude. Traditional search infrastructure was designed for interactive search—not for AI agents performing dozens or even hundreds of retrieval operations per request.
Fragmented AI stacks increase complexity

Many organizations have successfully combined vector databases, search engines, rerankers, inference services, and orchestration frameworks. While this works well for early AI applications, every additional component increases operational complexity and makes the platform harder to evolve.
Scaling AI increases costs

As AI workloads grow, inefficient architectures become increasingly expensive to operate. Optimizing where retrieval, ranking, and machine learning execute is becoming essential for controlling infrastructure costs while maintaining AI quality.
Performance impacts user experience

Users expect AI to respond instantly. Maintaining low latency while supporting larger datasets, more sophisticated models, and dramatically higher retrieval workloads is becoming a key competitive differentiator.

Cost

The Cost of AI Search at Scale

Many AI search architectures evolved by adding specialized technologies as new capabilities emerged. For first-generation AI applications, stitching together search engines, vector databases, rerankers, feature stores, and inference services proved an effective approach for delivering semantic search and RAG.

As AI retrieval workflows become increasingly iterative, the challenge is no longer simply finding the right information. Every retrieval, reranking pass, and model invocation consumes valuable compute resources. Without careful control over where and when expensive computation occurs, infrastructure costs become increasingly difficult to predict and manage. At scale, compute efficiency becomes just as important as retrieval quality.

The compute challenge is compounded by the architectural complexity of fragmented AI search stacks. Every additional service introduces integration overhead, network latency, operational burden, and infrastructure cost.

Defeating the Integration Tax

According to GigaOm's CIO Decision Brief, organizations consolidating fragmented AI search stacks can achieve:

Up to 5× lower infrastructure costs through a unified AI Search Platform.
Recovery of engineering capacity, redirecting teams from maintaining synchronization pipelines to building new AI capabilities. GigaOm estimates that reclaiming just three engineers represents over $540K per year in recovered engineering investment.
Fewer vendor relationships and simpler procurement by replacing multiple AI search components with a single platform.

Read GigaOm Decision Brief: Defeating the Integration Tax in AI Search

As AI agents become mainstream, GigaOm argues that organizations must rethink whether fragmented AI search architectures remain economically sustainable.

Choose Vespa When:

AI retrieval costs are difficult to predict and budget for.
You are transforming an information platform into an AI-native application.
You need AI agents that investigate proprietary knowledge.
Your AI search stack is becoming too complex.
Search quality is becoming your competitive advantage.

Customers

Vespa at Work

AlphaSense

AI-powered market intelligence over 500 million premium business documents.

AlphaSense combines proprietary business content with AI-powered search built on Vespa's AI Search Platform to help professionals investigate, reason, and make faster decisions across finance, life sciences, and corporate strategy.

Perplexity

Delivering AI answers at internet scale.

Perplexity relies on Vespa to power retrieval across the public web, supporting fast, accurate answers with the performance required by millions of users.

Read Perplexity case study

RavenPack

Transforming proprietary financial data into actionable intelligence.

RavenPack uses Vespa to power AI search across millions of financial documents, helping AI agents investigate proprietary financial data and deliver trusted insights for investment professionals.

Why Vespa

One Platform. Every AI Search Capability

Building AI search doesn't require another search engine or another AI service. It requires a platform that integrates retrieval, ranking, machine-learning inference, and real-time serving into a single distributed architecture.

By executing these capabilities close to the data, Vespa reduces architectural complexity, improves performance, and helps organizations build AI search, conversational experiences, and AI agents over proprietary information.

Why Leading Organizations Choose Vespa

Replace fragmented AI search stacks with a single platform for retrieval, ranking, machine learning inference, and real-time serving—reducing operational complexity while accelerating AI innovation.
Support iterative retrieval, reasoning, and multi-step AI workflows over proprietary data without the latency and infrastructure costs of stitched architectures.
Combine proprietary content, structured data, operational systems, and external sources through standard APIs and SDKs without duplicating data across multiple search systems.
Support hybrid search, vector search, personalization, multimodal search, and RAG from a single search platform that evolves with your AI strategy.
Power billions of documents, real-time updates, and thousands of concurrent queries with predictable latency and efficient resource utilization.
Deploy on Vespa Cloud with automatic scaling and transparent pricing, or self-manage wherever your architecture requires. Built with enterprise-grade security and governance from the ground up.

Ready to Build Your AI Search Platform?

Building the next generation of AI applications requires more than adding another service to your stack. Explore how Vespa's unified architecture simplifies AI retrieval while helping control infrastructure costs as you scale.

Explore the AI Search Platform architecture

What do you mean by "information platforms"?

Information platforms is our umbrella term for organizations that create value by helping customers discover, analyze, and act on proprietary information. This includes companies in market intelligence, financial data, legal research, scientific publishing, media archives, business information, and other knowledge-rich domains. While these organizations serve different industries, they share a common challenge: transforming proprietary data into AI-native experiences that deliver trusted insights quickly, accurately, and at scale.

Why are information platform providers becoming AI companies?

Users increasingly expect AI to investigate, summarize, reason, and deliver trusted answers over proprietary data rather than requiring them to search and interpret information themselves. Whether delivering financial intelligence, legal research, scientific knowledge, or competitive insights, AI is rapidly becoming the primary interface through which customers discover, consume, and act on information.

Why does agentic AI change search architecture?

Traditional AI applications typically perform one or two retrieval operations before generating a response. AI agents repeatedly retrieve, rank, verify, and synthesize information as they investigate a problem. A single request may involve dozens or even hundreds of retrieval operations, placing much greater demands on latency, scalability, data freshness, and operational efficiency. Architectures that work well for first-generation RAG often become increasingly difficult to scale as AI agents become more capable.

Why choose a unified AI Search Platform?

Many organizations begin with separate search engines, vector databases, rerankers, and inference services. As AI applications evolve, maintaining and synchronizing these components becomes increasingly complex. A unified AI Search Platform brings retrieval, ranking, machine learning inference, and real-time serving together within a single distributed architecture, reducing operational complexity while providing a stronger foundation for AI search, RAG, and AI agents.

Can existing search platforms support AI agents?

Yes, but the architecture matters. Many organizations successfully deliver conversational search and first-generation RAG using stitched AI search stacks. As AI agents become more sophisticated, however, the number of retrieval operations, the need for real-time data, and the complexity of ranking all increase significantly. This is why many organizations are consolidating fragmented AI search stacks into unified platforms designed for continuous AI retrieval.

Scale AI Innovation. Not Infrastructure Costs.

Planning your next generation of AI applications? We'd be happy to discuss your architecture, answer your technical questions, and show how a unified AI Search Platform can help you innovate faster while keeping AI infrastructure costs under control.

Transform Proprietary Data into AI-Powered Intelligence