Algolia announced the acquisition of Velou, a New York-based product intelligence company, to strengthen the data layer beneath its search, recommendations, personalization, merchandising, and Agent Studio products. The move is intended to turn inconsistent catalog text and imagery into structured, retail-specific product data for more than 18,000 customers. Algolia said the acquisition does not change its direction but accelerates the intelligence underneath its entire platform.
The Catalog Data Problem
Many merchandising leaders recognize that product data arrives from hundreds of suppliers in inconsistent formats and often lacks the attributes shoppers actually search for. A customer may ask for a machine-washable navy midi dress for a fall wedding, while the catalog says only blue dress with a price and a size chart. Teams respond with synonyms, boosts, manual tags, redirects, and rules, but the work never finishes because catalogs change every day.
How Velou Enriches Product Records
Velou uses multimodal AI to understand product imagery and catalog content, creating the structured attributes a merchandiser would add with unlimited time. It maps details such as navy, midi, fabric weight, care instructions, and occasion against a curated retail taxonomy, normalizing different ways of describing the same thing. The system runs continuously at production scale and grounds every attribute in source evidence, because a confident wrong attribute can be worse than a missing one.
What Better Product Understanding Unlocks
When product records are richer, high-intent long-tail queries stop coming back empty and product facets fill in with filters such as occasion, fit, fabric, or care. New arrivals can rank based on what they are from launch day, reducing manual boosts, and teams can maintain fewer handwritten rules, synonyms, and redirects. Search, recommendations, and personalization then reason over the same enriched record, so the dress a shopper finds in search is the one recommended next to it.
Customer Evidence and Results
Algolia highlighted results from Velou retail customers as evidence of the approach. U.K. sportswear retailer Get The Label added more than 190,000 product attributes over 6 months and revenue from its site search rose 60%. At U.K. fashion retailer Everything5pounds, catalog attributes grew more than 85% and search conversions rose 33%, though the company said results will vary by catalog.
Integration, AI Readiness, and Inspectable Intelligence
Algolia said existing APIs, SDKs, and integrations stay exactly as they are, with no re-platforming, rebuilding, or new implementation work required. The enriched catalog also supports AI readiness because more shoppers now begin in ChatGPT, Microsoft Copilot, or Google Gemini, and these agents read product data rather than browsing pages. Adobe Analytics reported that AI-referred traffic to U.S. retail sites rose 62% year over year in July.
Transparency for Merchandising Teams
Every attribute carries its evidence, so when a product ranks, a team can see why and can fix the record instead of writing another rule. Algolia said it does not believe in black-box merchandising and asked whether merchandisers can see where an AI answer came from. This transparency is positioned as a key safeguard as vendors make broad AI promises.
Algolia plans to schedule a webinar for a live walkthrough of the technology on a real catalog. The company said the next shopper who types a very specific question into a search bar or an AI assistant should find exactly the product they described. That outcome, according to Algolia, is the work now underway across its platform and customer base.