Transform Proprietary Data into AI-Powered Intelligence

Data Platform Providers Are Becoming AI Companies

Data platform providers—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 data 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 data platform providers, AI is no longer simply another feature—it is becoming the primary way customers discover, consume, and act on information.

Building AI-Native Data Platforms Isn't Easy

Delivering these experiences requires far more than adding an LLM to an existing search stack. AI agents repeatedly retrieve, rank, filter, verify, and synthesize information from massive proprietary datasets, placing far greater demands on search infrastructure than traditional AI applications.

Many organizations have successfully delivered first-generation AI experiences by stitching together vector databases, search engines, rerankers, inference services, and orchestration frameworks. For conversational search and simple retrieval-augmented generation (RAG), this architecture can be effective.

As AI agents become more autonomous, they perform increasingly iterative retrieval workflows, often executing dozens or even hundreds of retrieval operations in response to a single request. Every additional retrieval amplifies network latency, operational complexity, and infrastructure cost. What was once an acceptable architecture quickly becomes difficult to scale economically.

Data platform providers now face two converging challenges: consolidating fragmented AI search stacks while simultaneously supporting increasingly sophisticated AI agents. Together, these trends are reshaping the industry.

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. 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.

As GigaOm concludes, the question is no longer simply how to add AI search, but whether fragmented architectures remain viable as AI agents become the primary interface to proprietary data.