COMPUTER VISION FOR FOOD PRODUCTION

Camera checks, violation logs and the quality team help maintain order. But manually following every staff action, product and production-line event is impossible.

Violations are detected late, and consequences become known after production.

Hygiene requirement violations

Staff enter production zones without required protective clothing, depart from movement procedures or skip visible hygiene steps.

Supervisors and quality staff cannot continuously monitor every zone and shift. This creates risks of product contamination, inspection findings, investigations and batch withdrawals.

Automatic AI-based recording immediately notifies the people responsible.

Raw-material theft

Ingredients and finished products disappear from storage or production areas, causing unexplained inventory discrepancies. Cameras record events, but manually tracking bags, boxes, containers and products around the clock is impossible.

This leads to direct financial losses, shortages, higher unit costs and investigation expenses.

Computer vision records suspicious raw-material movements.

Product defects

Damaged packaging, incomplete sets, visible product defects or incorrect labeling are discovered late.

Spot quality checks cannot inspect every item on a high-speed line. This leads to write-offs, returns, claims and extra logistics or shortage costs.

Our system can monitor the entire production line.

Process-operation violations

Staff skip required actions, change the operation sequence or depart from procedures.

Instructions are documented, but monitoring depends on a manager's presence and limited attention. The result is inconsistent quality, spoiled materials and costly reprocessing.

Monitoring visually observable steps allows events to be compared with process sheets.

Downtime and lost working time

A line stops, an operator leaves the area or finished products accumulate at handoff. Causes are not always recorded, and manual video review takes time.

Recording machine stoppages and deviations immediately notifies the production manager.

Warehouse picking and dispatch errors

Boxes are packed incorrectly, pallets move to the wrong zone and batches are mixed. The operational system shows recorded transactions but does not always confirm the actual physical action. Mispicks, shortages, returns and additional order-handling costs follow.

Matching video events with WMS, ERP and mandatory product marking.

The problem is not always a lack of procedures. More often, it is the inability to check compliance continuously.

Computer vision transfers this part of manual monitoring to an automated system that records defined events and helps responsible staff make management decisions faster.

SCENARIO CATALOG

• Sanitation and hygiene / Product quality / Processes and staff / Warehouse and dispatch
• Protective clothing and PPE checks
• Missing required caps, masks, gloves or other visually distinguishable clothing items.
• Clean-zone entry monitoring
• An employee passes through a defined zone without an observable required action.
• Hygiene procedure compliance
• Recording visits to hand-treatment stations, action sequences or missed visible steps.
• Cleaning and washing checks
• Comparing observed actions with an approved schedule and checklist.
• Restricted-zone access
• Detecting outsiders or unauthorized people through Face ID in a monitored zone where the necessary data is available.

Don't see the task you need?

We develop computer vision around your internal procedures and equipment specifics. Let's discuss which processes can be automated at your site.

From a production violation to a notification for the responsible person, automatically

The system analyzes feeds, detects defined events and passes information to staff responsible for quality, safety or production.

Camera — records the area

AI model — finds a deviation

System notification — received by the responsible person

Review — records the outcome and may escalate a violation to a senior manager

Example:

• An employee enters a production zone without the required head covering.
• The system detects the missing protective clothing item → saves a video clip and event time → notifies the responsible person → creates a record for review and analysis of repeated violations.

How much does late defect detection cost your production site?

Computer vision helps reduce losses through earlier deviation detection, less manual monitoring and faster responses to violations, rather than by replacing employees.

Four sources of business impact:

Source

How savings arise

Defects and write-offs

Early detection of defects and packaging errors before subsequent costly operations

Downtime

Less time between an observable deviation and the response

Manual monitoring

Less time reviewing recordings and searching for violations

Claims

Fewer defects reaching dispatch, subject to validated detection performance

Potential-savings calculator

Estimate the reduction in defect losses

Example:

• Annual: RUB 1,800,000

Want to assess your site's economics? We will show which monitoring scenarios suit your equipment and how to validate their effectiveness.

INDUSTRY SOLUTION EXAMPLE

Building automated quality and procedure monitoring in food production

We explore violations that can be detected automatically, linking cameras with production data, management reports and pilot validation.

20 potential scenarios

4 monitoring areas

7 presentation screens

Computer vision is our specialty. We develop solutions for your specific processes.

• We go beyond connecting existing video analytics. We develop and train CV models, design server architecture and integrate systems into site workflows.
• We develop our own computer vision models
• We work on individual tasks, from defect detection to monitoring process-operation sequences.
• We save your resources by finding unconventional ways to optimize compute capacity and server load. We select models and GPUs for the number of feeds, required processing speed and operating costs.
• Integration with your business processes
• We connect computer vision events with ERP, MES, WMS and other available data sources.
• Post-launch support
• We provide monitoring, maintenance and solution development. An agreed 24/7 technical support arrangement is available.

You do not need to work out GPUs, server configuration or video-processing architecture yourself. We select a suitable deployment model and maintain the system.

You have servers

• Deployment in your private environment
• We assess resources, deploy local models, optimize load and configure video processing within the company's protected infrastructure.
• No mandatory server rental
• Data remains inside the company
• Existing equipment is used where sufficient

No servers

• We provide rental infrastructure
• We select and provide rental compute capacity, deploy models and configure monitoring and technical operations.
• No need to buy your own GPU server
• We handle configuration and maintenance

In both cases, we can connect existing cameras, integrate with corporate systems and configure dashboards.

Start with a small pilot. Scale after validating the result

01 Free audit

We examine processes, observation zones, camera resolution, server capacity and data sources. We identify scenarios that can be automated.

02 Paid pilot

We develop and adapt models, connect a limited number of cameras, and configure events and dashboards. We test accuracy, speed, false alerts and operation on real data.

03 Scaling

We extend the solution to additional lines, production areas and sites. We connect agreed integrations and workflows.

04 Technical support

We monitor operation, resolve failures, update models and adapt to changing conditions. We support operation after deployment.

Before the pilot, we agree which events must be found, how accuracy is tested and which acceptance criteria apply.

At the demonstration, you will see computer vision examples and can describe your site's tasks.

We will discuss which violations to monitor, whether existing cameras are suitable, which infrastructure to use and where to start the pilot.