AI Search for Financial Services

industry trend

AI Creates New Customer Experiences

Leading financial institutions are adding AI search, conversational experiences, and AI agents to their customer-facing applications. Customers increasingly expect AI to explain products, compare options, summarize financial information, and help them complete complex financial tasks. Delivering these experiences is becoming a competitive differentiator, improving customer satisfaction, strengthening loyalty, and driving revenue growth.

The challenge is no longer deciding whether to add AI, but building the architecture to support it. AI agents repeatedly retrieve, rank, reason over, and verify information before generating recommendations, placing far greater demands on retrieval, ranking, and machine-learning infrastructure than traditional search does.

Vespa powers some of the world's largest AI-native applications. Our AI Search Platform helps organizations build AI-native products with predictable performance and infrastructure efficiency.

challenge

Realizing the AI Opportunity

Leading financial institutions are creating more intelligent customer experiences with AI. Delivering these experiences efficiently introduces architectural challenges that many existing search stacks were never designed to handle.

Customer experience drives growth

Financial institutions that deliver more personalized, intelligent customer experiences will differentiate through higher engagement, stronger customer loyalty, and increased revenue.
AI agents increase workload

Financial AI agents repeatedly retrieve, compare, verify, and synthesize information before making recommendations. Traditional search infrastructure was never designed for this level of iterative retrieval.
Trust depends on relevance

Financial decisions require accurate, explainable answers grounded in trusted customer, product, and market data.
Scaling AI increases costs

Every retrieval, ranking stage, and model invocation increases compute requirements. Efficient AI architectures become essential for controlling infrastructure costs.
Performance builds confidence

Customers expect immediate responses. Delivering personalized financial guidance requires predictable low-latency performance even as AI workloads continue to grow.

Cost

The Cost of AI Retrieval at Scale

As AI workloads grow, fragmented architectures become increasingly expensive to operate. Every additional search engine, feature store, inference service, or ranking component adds network latency, operational complexity, and infrastructure cost. At scale, controlling where and when expensive computation occurs becomes just as important as retrieval quality.

Vespa brings retrieval, ranking, machine learning inference, and real-time serving together within a single distributed architecture, helping organizations simplify operations while delivering faster AI experiences at lower 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 workloads continue to grow, organizations must decide whether fragmented AI architectures remain sustainable or whether it's time to simplify AI retrieval with a unified platform.

Choose Vespa When:

As AI capabilities are added to customer-facing applications, you need an architecture that maintains efficient AI retrieval without increasing infrastructure complexity or cost.

You are adding AI search, conversational experiences, or AI agents to your financial applications.
You need AI to combine trusted customer, product, and market information.
You want to avoid a fragmented AI search stack.
You need predictable AI infrastructure costs as AI adoption grows.
Customer experience depends on fast, accurate AI responses.

Customers

Proven at Scale

Representative customer examples demonstrate Vespa's ability to power AI-native applications across internet-scale search, financial intelligence, and real-time analytics.

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

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.

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-native financial applications 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 trusted financial data.

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.
Build AI applications that align with existing security, governance, and data residency requirements. By bringing computation to the data, Vespa minimizes data movement, reduces latency, and helps organizations maintain compliance. Mutual authentication, encryption in transit, and encryption at rest protect sensitive information throughout the platform.

Ready to Build AI-Native Financial Services?

Leverage Vespa's experience powering some of the world's largest AI-native applications to simplify your architecture, accelerate AI innovation, and control infrastructure costs.

Explore the AI Search Platform architecture

Why are financial institutions adding AI to customer-facing applications?

Customers increasingly expect AI to explain products, compare options, summarize financial information, and help them complete complex financial tasks. AI is becoming a key differentiator for customer experience, helping financial institutions improve engagement, strengthen customer loyalty, and deliver more personalized services.

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.

How can financial institutions use AI while protecting sensitive customer data?

AI applications must operate within existing security, governance, and data residency requirements. Vespa brings computation to the data, reducing unnecessary data movement while protecting sensitive information through mutual authentication, encryption in transit, and encryption at rest. This allows organizations to build AI-native applications without compromising existing security policies.

Scale AI Innovation. Not Infrastructure Costs.

Leverage Vespa's proven platform and experience powering some of the world's largest AI-native applications. We'd be happy to discuss your architecture, share lessons learned from building AI at scale, and help you deliver customer-facing AI applications with predictable performance and infrastructure efficiency.

Powering AI-Native Financial Services