It’s Time for Offline Retailers to Use Data Better and Easier.
For More Precision Indoor Positioning
UWB _ Ultra Wide-Band
Ultra-Precise Indoor Positioning
UWB provides centimeter-level positioning accuracy, enabling highly precise indoor location tracking. Compared to GPS or BLE, distance errors are significantly reduced, allowing accurate detection of customer positions and movement paths inside stores. This enables advanced retail use cases such as shelf-level analytics and location-based marketing.
Robust and Reliable Positioning
UWB uses ultra-short pulses, making it highly resistant to signal interference and multipath effects. It maintains stable distance measurements even in complex indoor environments with people, walls, and fixtures. As a result, it delivers highly reliable location data in crowded commercial spaces.
For a More Intuitive User Experience
sLLM _ Specialized Large Language Model
Context-Aware Analysis
sLLM is a domain-specific language model optimized for specific business domains and data structures, designed not as a general conversational AI but as an AI purpose-built for analysis and decision-making.
By understanding predefined metric frameworks, data relationships, and business rules, it interprets user intent not as simple questions, but as analytical requests. Especially in complex data environments such as retail, sLLM maintains consistency in metric interpretation, period-over-period comparisons, and analytical perspectives, enabling step-by-step reasoning and insight generation similar to how human analysts approach analysis.
Advanced-RAG Architecture
Advanced-RAG applies a meta-only approach to overcome the efficiency and accuracy limitations of traditional full-text and chunk-based RAG. It filters out irrelevant documents using metadata and preserves document-level context during answer generation, minimizing chunk fragmentation. As a result, the system maintains stable indexing size while delivering high-quality, scalable knowledge retrieval and response generation.