Turning existing camera feeds into workflow insight

Client Industry

Multi-site retail · always-on store operations

Key Challenge

No consistent view of how in-store work & time are distributed

Core Tech Stack

Computer vision · Deep learning · Activity recognition · Video analytics

Deployment Model

Modular, per-format components over existing camera infrastructure

SoftmaxAI engineered a computer-vision system that turns a retail operator's existing camera infrastructure into workflow insight — reading everyday store footage to recognize common work activities and rolling the results up into clear, aggregate reporting. The system classifies tasks such as checkout, stocking, cleaning & customer assistance, then aggregates and anonymizes those observations into workflow-level views for managers. It is deliberately output-focused: the goal is a shared picture of how work and time are distributed to support staffing and scheduling — not raw footage, individual tracking, or surveillance of any single worker.

• Existing infrastructure sat underused. Although the operator had already installed cameras in its stores, the footage obtained from them provided no structured understanding of how the work actually took place — it had been recorded, not analyzed.
• Work distribution was invisible. In the case of continuous operations, managers did not have a consistent overview of the time spent on routine activities such as checkout, stocking, cleaning and customer assistance, which made it necessary to guess when allocating tasks.
• Store formats varied. It was impossible for a single fixed model to be suitable for all locations since the layouts, the positions of the cameras, and the workflows varied so much that the approach had to be adapted for different formats without requiring a complete rebuild each time.
• Insight had to respect people. The analysis had to remain at an aggregate level and focus on outputs — that is, it had to deal with workflows rather than assessing or keeping an eye on individuals — thus determining from the beginning what the system was permitted to produce.

The system functions as a modular, left-to-right video-analysis pipeline whereby the existing camera feeds pass through the ingestion stage, are then processed by deep-learning activity recognition, and finally go to an aggregation layer which anonymizes the observations before they are passed on to workflow reporting or the staffing signals.

• Spec before code. We prepared a specification that had been approved by the client, which defined the activities to be recognized, the workflow reporting that the client required, and the specific limits of the respectful, aggregate-only analysis before proceeding with the writing of the production code.
• Vision models for activity recognition. Senior engineers carried out an agent-accelerated build of computer-vision and deep-learning models which are capable of interpreting video feeds in order to classify the activities relating to checkout, stocking, cleaning and customer assistance based on the footage that is already being captured.
• Modular by design. The pipeline was constructed as a set of modular components so that the same method can be applied regardless of the store format or camera arrangement — instead of re-engineering the entire system, the relevant parts are either swapped or retuned for each location.
• Human-owned interpretation. The aggregation layer anonymizes the observations and combines them into summaries at the workflow level, and it is up to the humans to determine how those insights are interpreted and put into practice — the system provides the basis for staffing decisions but does not make them itself.

• More value from cameras already installed. Current feeds act as a continuous source of understanding of the workflow, converting the infrastructure that the operator already had into useful, structured insights.
• A consistent view of how work is distributed. Managers obtain a common, central overview of the way time is spent on various routine store tasks, replacing reliance on intuition with the patterns observed in the workflow.
• Aggregate reporting, not raw footage. Instead of showing the video, the system displays workflow-level rollups and foot-traffic signals, ensuring that the output remains focused on operational aspects and respects the people who appear in the footage.
• Adaptable across store formats. Because of its modular design, the same method of recognition and aggregation can be adjusted to suit different retail environments without the need to rebuild it entirely.
• Better-informed staffing decisions. The signals derived from the tasks and the amount of foot traffic are used to make decisions about staffing and scheduling, enabling managers to arrange the work in accordance with the way it actually flows.