How It Works
From data collection to production deployment—here's the complete workflow from capturing images to running automated verifications in the field.
The Four-Step Process
Capture images with any camera
Draw boxes & tag what matters
System learns from your examples
Run automated checks in production
Step 1: Collect Data
Capture Images of What You Want to Detect
Use whichever platform works best for you—capture in the field, at your desk, or upload existing images.
- Any camera works: Phone, webcam, DSLR, security camera—any image source is supported.
- Take variety: Different angles, lighting, and distances help the model learn better.
- How many? Start with 5 images. More is better, but you can always add more later.
- Works offline: Field apps work offline and sync when connected.
Import Existing Images
Already have a dataset? Upload from your computer, cloud storage, or import from existing archives.
Step 2: Label Your Data
Draw Boxes Around What Matters
This is where you teach the model what to look for.
Our Innovation: Center-to-Corner Drawing
Unlike other tools that make you drag from corner to corner, we make it easier:
- Tap the center of the object
- Drag to the corner
- Box expands symmetrically from center
- Release to save
Why this matters: On a phone, tapping a center point is way easier than trying to hit an exact corner. This saves time and reduces mistakes.
Tag What You Found
After drawing a box, tell us what it is:
- Select from your class list (e.g., "hard hat", "safety vest", "hazard")
- Or create a new class on the fly
- Quick number keys for fast labeling (press 1 for first class, 2 for second, etc.)
Work as a Team
Split the labeling work:
- Assign images to team members
- Each person labels their batch
- Manager reviews and approves
- Quality control built-in
Step 3: Build Your Solution
One-Click Training
Once you have labeled data, click "Train Model" and we handle everything:
- Automatic model selection: We choose the right architecture for your use case
- Hyperparameter optimization: We tune settings for best performance
- Validation: We test on unseen data to verify accuracy
- Performance metrics: You see exactly how well it works
Our cloud infrastructure handles all the complex machine learning, so you don't have to. Just click a button and get a working computer vision model.
Review Results
After training completes, you receive:
- Accuracy metrics: How often the model is correct
- Confusion matrix: What mistakes it makes (if any)
- Sample predictions: See it working on your test images
- Confidence scores: How sure the model is about each detection
Step 4: Deploy & Use in Production
Run Automated Verifications in the Field
Deploy your model to any device and start running automated checks. Workers capture images on their devices, and computer vision instantly verifies compliance, detects defects, confirms task completion—whatever you've trained it to do.
Real-World Usage
- Field workers: Take photos on mobile devices, get instant feedback
- Production lines: Camera feeds continuously monitor quality
- Managers: Dashboard shows all verifications, flags exceptions
- Automated alerts: Get notified when issues are detected
Track Performance
Monitor how well the system is working:
- Accuracy tracking: See detection rates in real conditions
- Confidence levels: Identify uncertain cases that need review
- Usage analytics: How many checks per day, who's using it, where
- Compliance reports: Audit trails with timestamps and photos
Flexible Deployment
Works wherever you need it:
- Mobile devices: Field workers use phones or tablets
- Fixed cameras: Production lines, entrances, workstations
- Desktop apps: Office staff review and verify
- Cloud or on-premise: Your data stays where you need it
Real-World Example
Construction Site Safety Compliance
The Challenge: Construction company needs to verify workers wear safety equipment at job sites. Manual checking is inconsistent and time-consuming.
Step 1: Collect (1 day)
- Safety manager takes 100 photos of workers at various sites
- Mix of compliant (wearing PPE) and non-compliant workers
- Different lighting, angles, and weather conditions
Step 2: Label (2 hours)
- Draw boxes around: hard hats, safety vests, goggles, gloves
- Tag missing equipment as "no PPE"
- 50 photos labeled by manager, 50 by assistant
Step 3: Train (30 minutes)
- Click "Train Model", select "Balanced" option
- System trains model, achieves 94% accuracy
- Review shows it correctly identifies PPE and violations
Step 4: Deploy (1 hour)
- Install app on site supervisors' phones
- Set up daily check-in requirement
- Configure alerts for PPE violations
- Dashboard shows compliance rates by site
Results
- 95% compliance within 2 weeks (up from 70%)
- 30 minutes/day saved on manual inspections
- Complete audit trail with timestamped photos
- Zero safety incidents since deployment
Continuous Improvement
Your Model Gets Better Over Time
Computer vision isn't "one and done." As you use it, you can make it better:
- Collect edge cases: When the model makes mistakes, save those images
- Label corrections: Show the model what it got wrong
- Retrain: Add the new data and train again
- Deploy update: Models automatically update on all devices