The problem: manual inspection doesn't scale
Most quality control, measurement or defect-detection processes in manufacturing, agriculture or logistics still rely on a person looking. That works until volume grows, shifts change, or fatigue sets in. The result: inconsistent criteria between inspectors, partial sampling instead of full inspection, and defects caught late — after they've already generated cost.
The problem isn't a lack of effort from the team. It's asking a human eye to do a high-volume, repetitive measurement job it wasn't designed for.
How we solve it
We build computer vision models trained specifically for your process — not a generic off-the-shelf model. The technical approach varies by case, but the pattern repeats:
- Standardized capture: we define how the image is taken (angle, lighting, distance) so the model receives consistent input. In Spray Analyzer, that's a photo under 365nm UV light with a standard phone camera.
- Segmentation and classification: the model isolates what matters in the image (a leaf, a part, an area) and classifies each region or pixel against what we're measuring or detecting.
- Calculation and decision threshold: we turn the classification into a number or an alert — coverage percentage, defect count, optimal/low status — with a measured margin of error, not an eyeballed one.
- Export and integration: results come out in the format your team already uses (Excel, dashboard, API), not a new interface to learn.
Expected result
The actual result depends on the specific process, and we quantify it during the initial assessment — not before. As a real-data reference: in Spray Analyzer, our verified computer vision case in agriculture, the system processes each photo in seconds and has measured over 10,000 leaves in real production, replacing a process that previously required a lab or physical spray cards.
For industrial inspection processes without a documented case yet, we work with directional estimates based on comparable projects — always labeled as such until we have real data from your process.
Technology stack we use
We choose tools for what they solve, not for trend. Here's what actually sits behind a computer vision project with us:
Python + OpenCV
Image processing and fast prototyping — the base of nearly any vision pipeline.
PyTorch / TensorFlow
Training classification and segmentation models, depending on the case and available data volume.
YOLO
Real-time object detection when the case requires identifying multiple elements per image.
scikit-learn
Simpler, more explainable models when a full neural network isn't justified.
AWS / Azure / Google Cloud
Model deployment on whichever cloud your company already uses — no forced provider migration.
Methodology
Every project follows the same five phases, with a clear deliverable at each one — from initial assessment to production monitoring. See full methodology →
Where it applies today
We work in computer vision for agriculture and agtech (our most demonstrable sector, with Spray Analyzer in production), and we evaluate cases in manufacturing, logistics, retail, security and health depending on the specific process.
Frequently asked questions
What's the difference between computer vision and a regular security camera?
A camera only records. A computer vision system interprets every frame with an AI model: it counts, measures, classifies or detects anomalies in real time, turning that into an actionable data point or alert.
Do I need special cameras or hardware?
It depends on the case. In Spray Analyzer, our real case, a standard phone camera and a UV lamp are enough. Other processes may need an industrial camera, but we always start by assessing what hardware you already have before recommending anything new.
How long does a computer vision project take to implement?
It varies by process and the availability of training data. The initial assessment (Phase 1 of our methodology) determines the real scope before we commit to a timeline.
What if I don't have enough images to train a model?
It's the most common limitation, and we evaluate it during the assessment. Depending on the case, we use pre-trained models adapted to your process (transfer learning) or design a data-capture process with you before building the final model.