Past battle · 2024-09-20 UTC
AI Shopping Agents Showdown — September 20, 2024
From the AI Shopping Agents category. 12 marks placed across 2 fighters. Dori Chatbot took the crown.
Final standings
Dori ChatbotThe line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
Dori Chatbot
AI chatbot that turns online stores into conversational shopping experiences.
Dori Chatbot is a conversational commerce assistant designed to help online retailers engage shoppers through natural, chat-based interactions. It guides visitors from product discovery to checkout by understanding intent, recommending items, and answering questions in real time. By integrating with a store's catalog, Dori can surface relevant products, handle common pre-sale questions, and reduce friction in the buying journey. The goal is to replicate the experience of a knowledgeable in-store associate across digital storefronts, helping merchants improve conversion rates and customer satisfaction.
Criteria breakdown
- Natural language product search
- AI-driven recommendations
- Pre-sale and FAQ handling
- Store catalog integration
- Conversational checkout assistance
- 24/7 automated shopper engagement
Recomaze AI Agent
AI commerce layer that fixes catalog discoverability and runs a conversational sales agent on your store
Recomaze AI Agent is a commerce platform focused on making online stores discoverable and recommendable to AI assistants while also driving on-site personalization. It addresses a shift the company highlights: shoppers increasingly ask tools like ChatGPT, Gemini, and Perplexity for product recommendations, and many retail catalogs are not structured in a way these models can read or surface accurately. The product combines three main stages. A discoverability scan checks how a store appears across multiple AI engines for high-intent queries, identifying products and categories where competitors are recommended instead. A fix stage generates AI-ready titles, descriptions, Q&A, and structured data at scale across SKUs to improve machine readability. A sales agent stage deploys a conversational AI assistant on the storefront, trained on the store's catalog, that answers shopper questions and surfaces relevant products. It is aimed at e-commerce retailers, particularly those with large or multi-language catalogs, who want both improved visibility in AI-driven search and higher on-site conversion. Recomaze positions itself as a unified data layer that consolidates catalog, visitor behavior, and conversation data into a persistent memory that AI agents can read and act on. The company cites a deployment with Newpharma spanning over 45,000 products and 1,700+ brands, reporting added basket items and catalog sales increases. A dashboard surfaces ranked actions such as catalog attribute gaps, lost queries, agent conversions, and competitor mentions, alongside reference metrics like visibility score, conversation volume, and add-to-cart rate. As with any vendor-reported tooling, the cited statistics and conversion lifts come from the company and select customers, so results will vary by store, catalog quality, and traffic mix. Buyers should evaluate it against dedicated product recommendation engines, on-site search tools, and emerging AI search optimization services depending on their priorities.
Criteria breakdown
- Cross-engine AI visibility scanning
- Automated AI-ready titles, descriptions, and Q&A
- Structured data generation across SKUs
- Storefront conversational sales agent
- Competitor mention and query-loss tracking
- Unified commerce memory data layer