AI Builds on Product Discovery

Conversational shopping and agentic commerce don't replace product discovery—they depend on it. No amount of AI can compensate for a weak product discovery foundation. Every AI shopping experience relies on retrieving the right products, customer context, merchandising rules, pricing, and inventory before it can answer questions, make recommendations, or take action.

As AI shopping experiences become more sophisticated, the demands on product discovery increase. AI retrieval becomes the foundation behind every recommendation, response, and decision.

Conversational & Agentic Commerce

Conversational shopping and agentic commerce offer different customer experiences, but both rely on the same foundation: intelligent product discovery.

  • Conversational Shopping

    Help customers discover, compare, and evaluate products through natural conversation. AI shopping assistants answer questions, explain recommendations, and guide purchasing decisions using your product catalog and business knowledge.

  • Agentic Commerce

    Enable AI shopping agents to research products, compare alternatives, and complete tasks on customers' behalf. These autonomous workflows depend on fast, accurate retrieval across multiple reasoning steps before making recommendations or taking action.

Whether customers are interacting directly with an AI shopping assistant or through an autonomous AI agent, the underlying challenge is the same: retrieving accurate product information, customer context, inventory, pricing, and business rules quickly and reliably. The more autonomous the shopping experience becomes, the more important retrieval becomes.

AI Agents Need Better Retrieval

AI agents depend on retrieval that executes accurately, efficiently, and repeatedly throughout every stage of a task. While they can reformulate queries, explore alternative retrieval paths, and gather additional evidence autonomously, they remain limited by the quality, freshness, and completeness of the information they retrieve.

Unlike human users, AI agents can automatically construct precise queries, combine multiple retrieval techniques, apply structured filters, and refine their approach as they gather new information. Rather than issuing a single search, they may perform dozens or even hundreds of retrieval operations, exploiting sophisticated retrieval capabilities that would be impractical for most human users.

Every retrieval influences the next, making powerful query execution, ranking, and real-time access to fresh data fundamental to the agent's overall accuracy, efficiency, and cost.

The AI Retrieval Workflow

Every successful AI agent follows the same high-level pattern: it plans how to retrieve information, executes that retrieval, evaluates the evidence, and refines its approach as understanding develops. This iterative process is the AI retrieval workflow.

How Agents Interact with Retrieval

AI agents plan retrieval; they do not perform it. The agent determines what information is needed and how to search for it. The AI retrieval workflow carries out that plan by retrieving, ranking, filtering, and enriching the most relevant evidence. Separating planning from execution allows each layer to specialize, producing more accurate, efficient, and scalable AI applications.

AI Agent (plans the investigation)
Vespa Retrieval (executes the plan)
Understand the task
Execute the retrieval plan
Decide what to retrieve
Find the best evidence
Refine the retrieval strategy
Retrieve, rank, and infer
Evaluate evidence
Apply business logic and ML
Decide when sufficient evidence is gathered
Return ranked results

Why Vespa for Agentic Commerce

Choose Vespa when your AI agents require:

  • Sophisticated retrieval strategies rather than a single search technique.
  • Accurate retrieval and ranking for reliable reasoning and decision-making.
  • Low-latency execution across repeated retrieval workflows.
  • Continuously updated data for trustworthy responses.
  • A unified retrieval architecture instead of stitched-together point solutions.
  • Production-scale performance for customer-facing AI applications.

Explore the AI Search Platform

Learn how the Vespa AI Search Platform combines retrieval, ranking, machine learning, and distributed serving in a single architecture to execute the end-to-end AI retrieval workflow for large-scale AI applications.

Explore the AI Search Platform

Ready to Extend Your Product Discovery Platform?

Whether you're introducing conversational shopping or preparing for autonomous AI agents, Vespa provides the product discovery foundation that powers intelligent shopping experiences. We'd be happy to discuss your architecture, answer technical questions, and explore how Vespa can support your AI strategy.

Contact us