Onki offers AICap, an AI‑driven, shelf‑mountable sales assistant that uses computer vision, voice conversational AI, and a touchscreen to engage shoppers in real time. It delivers personalized product recommendations and brand‑compliant promotions, boosting conversion and providing brands and retailers with actionable in‑store behavior insights.
Funding
Funding not disclosed
Founders
Product
Problem
Retail environments often rely on static signage or limited staff to convey product information and promotions, which can lead to missed sales opportunities, especially for high‑margin, knowledge‑intensive categories. Shoppers may lack personalized guidance, resulting in lower conversion rates and limited insight for brands about in‑store consumer behavior.
Solution
Onki’s AICap is an AI‑driven, shelf‑mountable sales assistant that combines computer vision, voice conversational AI, and a touchscreen interface to engage shoppers in real time. By observing and analyzing visual and audio cues, AICap tailors its tone and content to each customer, delivering expert product knowledge and personalized recommendations that align with brand guidelines. The system operates autonomously around the clock, promoting products, answering questions, and guiding purchase decisions without additional staff. Interaction data are captured and fed into a closed‑loop reinforcement learning model, continuously improving conversational effectiveness and providing brands with actionable insights into customer preferences and behavior.
Target Audience
Primary customers are consumer‑packaged‑goods brands and retailers seeking to enhance in‑store promotions for high‑margin, knowledge‑intensive categories such as wine, supplements, and personal‑care products.
Features
- Integrated computer‑vision and voice AI that detects and interacts with shoppers passing by the shelf
- Adaptive conversational engine that customizes tone, style, and content based on individual customer cues
- Touchscreen interface for supplemental visual information and direct user input
- Brand‑specific content management that ensures promotions follow brand guidelines and messaging
- Continuous reinforcement‑learning loop that optimizes engagement metrics from observational and conversational data
- Data capture module that aggregates product preference, consideration factors, and behavioral insights for analytics