Cimulate introduced CommerceGPT, the first AI-native platform built for agentic commerce. CommerceGPT introduces a new category of product discovery infrastructure, purpose built for the shift from human to agentic shopping experiences. It is powered by Cimulate’s proprietary distillation via simulation approach which generates synthetic transactional information gleaned from frontier models. This synthetic data eliminates the data disadvantage retailers face when trying to get value out of AI investments.
The launch also introduces the Cimulate MCP Server, which supports an emergent interface for agent-to-agent commerce. This capability supports a new buying flow: from prompt to product, within any answer engine. This helps retailers build their own agents who know how to talk to answer engine agents and can therefore get their products surfaced in ChatGPT, Perplexity and Claude.
Built for each client, MCP Server enables fast, hyper-contextualized responses by parsing real-time signals across sessions, products, and user behavior. MCP server also powers the contextual discovery engine, a dynamic orchestration layer that guides retrieval and ranking across product discovery.
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“This is what commerce looks like when AI works for the shopper,” said John Andrews, Co-Founder and CEO of Cimulate. “With CommerceGPT, brands can finally meet agents—and their human counterparts—where discovery begins: In the conversation.”
The foundation of CommerceGPT is Cimulate’s breakthrough training method, distillation via simulation, which simulates thousands of real-world shopping scenarios to build smarter, more nuanced models tailored to each brand’s catalog, context, and customer behavior.
“Retailers are at a data disadvantage – they’re limited leveraging AI based on their data, which is orders of magnitude smaller than retail giants like Amazon and Walmart. What we’ve created allows retailers to close this data gap and tap into all of the data on the internet,” said Vivek Farias, Co-Founder and Chief Technology Officer at Cimulate. “Our approach to building synthetic data and combining that with our customers proprietary data eliminates the data disadvantage.”
SOURCE: PRNewswire
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