P2ACH AI analyzes physical spaces so retail media, operations, and marketing teams can make faster decisions from the same trusted data.
The P2ACH AI Agent goes beyond dashboards. It reads offline behavior data, explains what changed, and recommends the next action.
It combines real-time vision analytics with history, KPI benchmarks, and operating rules, then interprets the data across people, space, media, products, and behavior.
By combining an LLM-based natural language reasoning engine with rule-based analytical logic, the AI Agent interprets user intent within the dashboard and automatically retrieves and analyzes the required data. It performs root-cause analysis, identifies key drivers of change, and detects anomalies.
Instead of only showing raw metrics, the system recommends actions for ad operations, store layout, and content strategy in physical spaces.
For Ultra-Precise Gaze Analysis
P2ACH AI applies it to retail spaces to correct distortion and field-of-view errors, then estimate the 3D position, direction, and coordinates of people or objects. That makes path and gaze analysis more accurate in physical spaces.
High Performance
When tracking is unstable, missed frames can cause the same person or object to be counted incorrectly. P2ACH AI reduces missed frames so ad performance, foot traffic, and heatmap analysis stay reliable.



We have confirmed technical excellence by significantly exceeding performance standards across all categories, verifying the precision of our AI models in ad-viewing area detection and achieving high accuracy in facial recognition factors such as gender and age estimation.
Delivering Efficiency and Cost-effectiveness
Two-camera processing expands the analysis area and supports front-and-back measurement for double-sided devices without adding a second AI Box.


Wide-Area Analysis


Double Side Angle Analysis
Retail Media & Insight

Universal High-Performance Edge AI
Our advertising performance analysis service uses only anonymized data and does not process personal information. The data anonymization process is as follows: