Algolia has acquired Velou, a New York-based company that uses multimodal AI to extract context-rich product data from catalogue text and images. Financial terms were not disclosed. Velou serves more than 50 retail and premium brand enterprise customers. The acquisition adds a layer of product intelligence to Algolia's platform, which handles nearly two trillion searches annually for over 18,000 customers. Velou's technology automatically extracts specific product attributes against a curated retail taxonomy, improving search accuracy without manual tagging. UK sportswear retailer Get The Label added more than 190,000 product attributes over six months using Velou, and revenue from site search rose 60%. The six-person Velou team joins Algolia. Existing APIs and integrations remain unchanged, with enhanced intelligence operating underneath.

Algolia launches production-grade MCP for agentic commerce. Developers can now connect ChatGPT, Claude, Gemini and custom AI apps to live product data without rebuilding retrieval for every framework. Algolia, the AI Search and Retrieval platform orchestrating more than 1.75 trillion queries each year, trusted by more than 18,000 businesses and used by millions of developers worldwide, announced the Algolia MCP Server, its application for accelerating the creation of AI agents, and optimizing agentic commerce experiences. As AI becomes the interface for commerce, developers face a fundamental challenge: Large Language Models (LLMs) can reason, but they cannot answer commerce questions without access to live product catalogs, pricing, inventory, merchandising rules and business context. Today, those integrations are often rebuilt for every model, framework and application. Algolia eliminates that work by exposing Algolia's commerce intelligence through a production-ready Model Context Protocol (MCP) foundation. Developers can connect any leading LLM (including ChatGPT, Claude, Gemini) and MCP-compatible agent frameworks to trusted product search and retrieval without rebuilding retrieval infrastructure for every AI experience. At the core of the release is Algolia's MCP Server, which exposes product search, facet discovery, catalog context and retrieval as standardized AI tools. The same MCP foundation powers both Agent Studio and direct MCP integrations across chat experiences, search, and agent experiences. AI-generated index descriptions further help LLMs determine which catalogs to query and when to query them, which improves retrieval quality without additional developer effort. Stephen Lynch, Chief Executive Officer, Algolia, said: "AI is creating an entirely new interface for commerce. The companies that win won't be the ones building the best chatbot, rather they'll be the ones exposing trusted commerce intelligence to every AI agent, application and customer touchpoint. Agent Studio gives developers that foundation through MCP, making it possible to build once and extend intelligent commerce anywhere." Unlike generic LLMs, Algolia MCP gives AI agents access to that same live commerce data in production, including pricing, inventory, relevance, and merchandising rules rather than recreating those integrations for every framework or application. Developers can expose a single trusted commerce foundation that works consistently across the rapidly expanding ecosystem of AI agents. The latest release focuses on three developer priorities: a production-ready MCP foundation, a build-once deployment model across every commerce surface and production-grade controls for running AI agents safely at scale. A Production-Ready MCP Foundation for AI Agents Algolia's MCP Server turns Algolia's retrieval engine into a reusable foundation that AI agents can consume directly. Product search, facet discovery, relevance signals and catalog context become standardized tools that can be used consistently across ChatGPT, Claude, Gemini, custom applications or any framework that supports the MCP. The result is a portable discovery layer for AI commerce that powers conversational shopping, AI search and guided selling, without duplicating business logic or retrieval infrastructure. Build Once. Deploy Across Every Commerce Surface Agent Studio provides developers with the components, SDKs and deployment path needed to move from prototype to production quickly. A redesigned Quick Setup experience in the dashboard guides developers through connecting a catalog, configuring system prompts, enabling MCP tools, defining agent behavior and establishing safety policies. Once configured, Agent Studio generates production-ready implementation code, allowing teams to deploy their first AI experience in minutes rather than weeks. Developers can then embed the same AI capabilities across every customer touchpoint using Algolia's UI SDK, including search bars, autocomplete, chat, product pages, mobile applications and custom storefront experiences. AI-powered search becomes another interface to the same retrieval foundation rather than a separate application, which allows teams to extend identical product intelligence across every surface. Prompt suggestions, conversational entry points and agent behavior remain fully configurable, which provides developers control over how AI interactions begin and evolve without requiring application-specific implementations. Because the retrieval layer is shared through MCP, the same commerce intelligence extends beyond storefront experiences to external AI clients and agent frameworks ensuring catalogs are available wherever customers choose to interact with AI. Production controls for AI Agents Agent Studio provides the operational controls developers need to run AI-powered commerce applications with confidence. Guardrails, governance policies and cost controls can be configured independently for every agent without requiring custom infrastructure or middleware. Developers can define input and output guardrails, restrict disallowed content categories, configure fallback behaviors and enforce moderation before responses reach end users. For streamed interactions, responses are evaluated before presentation, preserving the responsiveness users expect while ensuring generated content complies with organizational policies. Operational controls extend beyond safety. Teams can configure request limits, per-IP rate limiting, maximum token budgets, conversation depth, tool execution limits, approved domains and other production boundaries that help control costs, reduce abuse and ensure predictable agent behavior at scale. Together, these updates make Agent Studio more than an interface for building AI commerce experiences, they make it the production infrastructure for exposing trusted commerce intelligence to modern AI systems. For developers, this means moving beyond isolated AI experiments and toward commerce applications that scale safely across every model, framework and customer touchpoint.

From Search & Discovery to agentic commerce experience. Algolia is pushing Search & Discovery forward with increasingly sophisticated AI. Bayezon changes the architecture. Product Intelligence understands the products. Intent Intelligence understands the shopper's objective. Agentic Search determines how to fulfill it. Journey Optimization determines what should happen next. Agentic Storefront(TM) turns those decisions into the experience. Algolia is evolving Search & Discovery. Bayezon is creating the step change to Agentic Commerce Experience. Don't just make Search & Discovery more intelligent. Make the entire commerce experience agentic.