Lemrock offers a platform that ingests and structures product catalogs for seamless retrieval by large language models, enabling brands to provide intent‑driven product recommendations and in‑chat purchases. The solution includes real‑time analytics and a unified integration point that connects merchants to multiple AI conversational interfaces, streamlining discovery, recommendation, and checkout flows.
Funding
Funding not disclosed
Founders
Product
Problem
Brands and retailers struggle to make their product catalogs discoverable and purchasable within AI‑driven conversational interfaces, where traditional search and recommendation tools are ineffective. Without structured data that large language models can interpret, products remain invisible in chat‑based commerce channels, limiting sales and customer engagement.
Solution
Lemrock provides an infrastructure that ingests a merchant’s product catalog and transforms it into a format optimized for large language model (LLM) retrieval. The platform enables hyper‑personalized, intent‑based matching, allowing users to ask natural‑language queries and receive immediate, context‑aware product recommendations that can be purchased directly within the chat. Real‑time analytics surface query trends, intent signals, and catalog gaps, giving brands actionable insights to improve visibility and conversion across major LLM platforms such as ChatGPT, Gemini, and Mistral AI. By delivering a unified “agent storefront,” Lemrock connects brands to multiple AI ecosystems, handling discovery, recommendation, and transaction flows in a single, scalable solution.
Target Audience
Primary customers are brands and retailers that sell online and want to reach shoppers through AI‑powered chat and voice assistants, as well as e‑commerce platforms seeking to add conversational commerce capabilities.
Features
- Automated catalog ingestion and structuring for seamless LLM interpretation across leading AI platforms
- Intent‑driven product matching that personalizes results based on user profile, context, and interaction history
- Integrated checkout flow enabling “chat to cart” transactions within conversational environments
- Real‑time analytics dashboard showing query intent, search gaps, and performance metrics for continuous optimization
- Multi‑channel deployment across open web, search LLMs, and proprietary chat agents with a single integration point
- GDPR‑compliant, brand‑safe data handling with full control over product exposure and visibility