Know What Is in Every Pen.
Know What Changed.
Reconcile observed cattle counts, flag movement exceptions, and connect visual identity evidence with the EID/RFID workflow your feedlot already uses.
Installed and recording at Tuskegee University · Now enrolling commercial cattle feedlots
Built for Your Operation
The same platform, tuned to two very different jobs.
For FeedlotsRunning a feedyard?
Observed pen counts, movement exceptions, and visual identity evidence for feedlot operating teams.
Explore the feedlot platform
For ResearchersRunning a study?
Research-ready evidence exports, deployment planning, and collaborative study-design support.
Explore academic partnershipsThe Truth About Legacy "AI"
Most livestock monitoring runs on brittle algorithms that fail the moment you actually need them. We engineered ours to close the gaps that legacy ag-tech leaves wide open.
Lost in the dark. Standard color cameras lose useful detail when lighting changes.
Low-Visibility Coverage
Color and thermal capture extend evidence collection across day and night.
Weather outages. Poor camera placement and unprotected hardware create avoidable coverage gaps.
Livestock-Ready Deployment
Camera geometry, enclosures, and quality checks are designed for livestock environments.
Animals disappear. Animals hidden in crowded pens can create incomplete observations.
Occlusion-Aware Tracking
Quality controls surface obstructed views and coverage gaps for review.
Raw data dumps. Endless spreadsheets of numbers are difficult to evaluate in the moment.
Review-Ready Evidence
Plain-language review cues point people back to the observed behavior and source imagery.
Bandwidth throttling. Sparse snapshots can miss the context around a behavior change.
Edge Processing
Local processing reduces bandwidth needs while preserving higher-frequency evidence.
Cloud-only dependence. Connectivity limits can interrupt cloud-only evidence workflows.
Resilient Retention
Local storage and backups are designed to preserve reviewable evidence when connectivity is limited.
Identity Evidence at the Chute,
Measured Against Known Animals.
Our chute workflow captures face, muzzle, and ear-tag views, builds a reference gallery, and tests candidate matches against known identities. Muzzle segmentation works in video; biometric matching and passive re-identification remain under validation.
- Traceable Research RecordsLink each chute passage to existing tags or manifests while identity models are evaluated.
- Validation PartnershipsTest capture geometry, match accuracy, failure cases, and operating limits with feedlots and research teams.
Adaptable Vision Research
The current field deployment is built around cattle. Other species are research directions that require their own data, operating workflow, and validation.
Which pen does not reconcile tonight?
Tell us your head count, pen layout, current inventory process, and the exception you need to catch. We'll map the smallest useful pilot.
We build with grace — serving people, protecting life, and honoring creation. Learn more.