OmniBot

Classic shop search answers keywords. But customers rarely have keywords, they have intent: "I need a gift for my father who loves hiking, under 100 euros." This gap between search field and purchase intent is exactly where online shops lose revenue. OmniBot closes this gap. The system is built on a large language model with retrieval over the shop's actual product data. It understands requests in context, asks clarifying questions, and guides users to the right product through dialogue, with answers grounded in the real catalog instead of generic model knowledge. We built and validated the system with real product data.

The innovative conversational chatbot seamlessly integrated - a powerful AI solution for your e-commerce business.

Conversational commerce rarely fails at the model and often fails at the interface. So we designed the interaction patterns from the ground up: how dialogue and classic search interlock, how the system handles ambiguous requests, how product recommendations appear inside the conversation. The bot integrates into the existing shop interface instead of sitting next to it as a foreign object. OmniBot demonstrates how LLM-based guidance integrates into existing B2C applications: from model architecture to retrieval to interface. The building blocks transfer to any use case where users need to find the right item in large catalogs or knowledge bases.