Retail

ActcastAI

Gate One wants to visualize the advertising impact of its checkout-counter digital signage, “Famima TV,” installed at more than 10,000 stores nationwide, in order to properly assess advertising value.

We handle everything end to end — developing the AI technology, launching the edge AI camera system, and long-term ongoing operation across thousands of stores in all 47 prefectures — to perform the visual recognition analysis itself. To realize an AI camera system that must never stop and must never lose data, we build in stable operation from the OS level up, alongside remote operational monitoring, root-cause isolation when anomalies occur, and monitoring AI accuracy. We also protect privacy by extracting only viewer demographic attributes and viewing time on the edge, discarding the identifiable images on site rather than sending them to the cloud.

ActcastAI

Sogo & Seibu wanted visibility not only into customers who bought something, but into the number, demographics and movement of everyone who visited — so that floor initiatives and tenant strategy could be based on evidence.

We installed edge AI cameras in the ceilings of the sales floors to estimate visitor counts and attributes (gender, age group). In later phases we added ReID — vectorizing facial features to judge whether two sightings are the same person — so movement across floors could be followed. Because the AI analysis runs entirely on devices costing a few thousand yen, deploying many cameras across large floors does not inflate the cost. Footage is converted to metadata on the spot, and no personally identifiable information is sent to the cloud.

JR Hakata City runs OMO-format stores to widen the opportunities for a variety of businesses to open shop, and wanted to hand those tenants data they could use for marketing.

We installed edge AI cameras in the store to capture visitor counts and attributes (gender, age group), plus how long signage and displays were actually looked at. Because the AI analysis completes on a credit-card-sized, inexpensive device, the setup can be deployed quickly enough to suit a limited-run pop-up store.

To cut store operating costs and improve the shopping experience at the same time — removing overnight work such as swapping out promotional materials, and reducing waste.

An AI camera judges, on the spot, how long a customer lingers in front of a shelf and which shelf they reach toward. Identifying the position of the wrist turns “walked past the shelf” into “showed interest in that product,” and this is wired into the signage delivery system to switch what is displayed. The aim is to remove the job of replacing paper promotional materials altogether.

Car parks themselves remain largely undigitized, and parking data sits separately from the data of the retail facilities attached to them. The aim was to bring the two together into something usable for marketing.

Occupancy detection, licence plate recognition and vehicle orientation are handled alongside people counting and attribute analysis — all on the same edge AI camera. Being able to capture both vehicles and people on one platform is what makes joining parking and facility data possible. Running it on the low-cost “ai cast” device keeps the equipment cost viable even when deployed across many parking bays.

Staff training had become dependent on individual supervisors, with no objective view of how service was actually being delivered. Customer harassment at the counter was a chronic problem, but its causes could not be pinned down.

AI microphones at the service counter record conversations between customers and staff with speaker separation. Making the quality of service objectively understandable from audio data means the same data serves both training improvement and harassment countermeasures. The visible presence of a microphone also has a deterrent effect of its own. The edge AI automatically detects when speech starts and ends, so store staff need no training to use it — zero operational overhead.