Capture
Current architecture uses six cameras to capture the bird from multiple angles.
Plant-floor AI grading
Designed as a ceiling/over-line retrofit around an existing processing line, subject to site survey.
Developed and running on a live U.S. duck-processing line. Chicken validation is the next commercial program.
See it in action
Watch the line run while the system captures, detects, grades, and sends the event before the diverter.
Current architecture uses six cameras to capture the bird from multiple angles.
The current model pipeline has measured approximately 174 ms under the documented configuration.
Plant-defined grading rules and defect classes are agreed before validation.
Integration is scoped during site survey for the selected PLC configuration.
The documented end-to-end decision path is approximately 175–185 ms in the tested configuration.
Current architecture uses six cameras to capture the bird from multiple angles.
Bird presentation, mounting area, and line interface are confirmed during site survey.
Materials, washdown requirements, and electrical scope are finalized during configuration review.
The demo interface shows grading, system-check, reporting, and configuration workflows for review during validation.
See the assigned grade, confidence, defects, line speed, and PLC status in real time.
Verify cameras, lighting, model detection, I/O signals, and PLC connection before the line starts.
Track grade distribution, hourly volume, and the defect patterns that need attention.
Export weekly and monthly grading reports for operations and quality review.
Proof in the data
After each run, the system turns grading into an operations brief: distribution, throughput, rejects, top defect patterns, and the action that should happen next.
Line 2, whole bird grading
06:00 to 14:00 shift, generated at run close
"Grade A is 19.7%, below the configured target. Review today's top defects."Generated automatically after each production run.
The return
The ROI conversation should be simple enough for a plant manager and concrete enough for finance: labor displaced, grade value recovered, and the assumptions used to model both.
Reduce grading labor
Model the savings directly from headcount, shifts, and operating days.
Recover grade value
Consistent grading helps identify undergrading and supports targeted quality improvement.
Modeled from your headcount, shift pattern, and loaded labor cost.
Modeled from your grade mix and downgrade drivers.
Run data, assumptions, and validation path.
System specifications
Designed as a ceiling/over-line retrofit around an existing processing line, subject to site survey.
Three validation steps
Start with the level of proof that fits your team. Each path turns the conversation into evidence you can review internally.
Request the validation outlineAgree the bird population, defect classes, ground truth, metrics, and purchase trigger before testing.
Review the current duck-line proof that customer permission allows. Reference access is subject to customer permission and NDA.
Validate the system against your own birds and pre-agreed acceptance criteria before the purchase decision.
Before a plant walk
Straight answers on performance, integration, downtime, and support. Final acceptance criteria are set around your line and your product.

Start validation
Share the line, throughput, and plant context. Albert will help scope the site survey, validation population, acceptance criteria, and evidence path.