AI for the factory floor
and the field, not just the lab.
Visual inspection, edge inference, and predictive maintenance — validated on real field conditions, not a lab dataset that never has to run where the work actually happens.
Where the camera meets
the actual production line.
Manufacturing and AgriTech operations run on visual inspection, sensor data, and documentation that often has to work under low-connectivity, variable-lighting, and hardware-constrained conditions. The workflows that pay off: defect and quality inspection, predictive maintenance from sensor data, and document or knowledge workflows for field teams who aren't always online.
By workflow,
not by buzzword.
Defect and quality detection on the line. See vision AI & edge.
Running inference on-device where connectivity is limited or latency matters.
Anomaly detection from sensor and equipment data. See machine learning development.
Field documentation and compliance records processed from photos and forms. See document AI.
Systems designed to function with intermittent network access, not assume it.
Ongoing accuracy tracking once deployed, not a one-time validation.
What's specific
to field deployment.
- Dataset shift — a model trained on studio-quality images will underperform on real lighting, dust, and motion the actual line or field produces. Training data has to represent deployment conditions.
- Hardware validation — inference behavior on the actual edge device can differ from a training environment's GPU; validating on the real hardware is part of the release process, not optional.
- Low-connectivity operation — field and plant-floor environments often can't guarantee a stable connection; systems need to degrade gracefully, not fail silently.
- Ongoing drift — lighting, equipment wear, and seasonal conditions change after deployment; accuracy needs monitoring over time, not just at launch.
Line-speed grading
under real operating conditions.
accuracy on real-time line-speed defect grading, measured under the actual camera, lighting, and throughput conditions of the production line it runs on — not a lab benchmark.
Our approach to closing the lab-to-field accuracy gap is detailed in a related case study on validating against real deployment conditions rather than studio data.
What "91%"
actually means here.
A grading accuracy figure is only meaningful alongside the conditions it was measured under: line speed, lighting consistency, and the specific defect categories in scope. We report accuracy per condition rather than a single blended number, and we validate on the real hardware and camera setup before a model goes live — not on a lab dataset that may not represent the actual line.
Training and deployment,
deliberately separated.
Separating training from deployment this way means the edge device's hardware constraints don't have to shape every training decision — but they do shape everything between training and shipping, which is where most lab-to-field accuracy gaps actually originate. A meaningful share of this pipeline work is delivered by our India-based engineering team.
Measured per condition,
not as one number.
| Metric | Measured against |
|---|---|
| Accuracy per defect category | Real field conditions at actual line speed, not a lab test set |
| False-negative rate on critical defects | Set lower than the general accuracy bar — a missed critical defect costs more than a false alarm |
| Inference latency on target hardware | The actual edge device, not a GPU-backed training environment |
| Uptime under intermittent connectivity | Graceful degradation tested deliberately, not assumed |
Before you
book a call.
Will the model work as well as it did in testing?
Only if it's validated on your actual field conditions — lighting, camera, and hardware — before deployment. See our vision field accuracy case study for exactly this gap and how we close it.
Does it need a constant internet connection?
No — edge inference is designed to run with intermittent connectivity, degrading gracefully rather than failing silently.
What happens as equipment or lighting conditions change?
We build in ongoing accuracy monitoring and a feedback loop for low-confidence cases, not a one-time launch validation.
Can this run on our existing edge hardware?
We validate on the specific hardware you'll deploy to before going live — inference behavior can differ from a training environment.
What does the training-to-deployment pipeline actually look like?
Training in PyTorch, export to ONNX Runtime for portable on-device inference, and field validation against real camera and lighting conditions before anything goes live. Training and deployment are deliberately separated so the edge device's constraints shape the export and validation steps, not every training decision.
Tell us about
your actual line conditions.
Book a 30-minute call — bring sample field footage or sensor data for an honest read on feasibility.