Computer Vision
We build computer vision systems that turn images and video streams into structured, actionable data — detection, classification, segmentation, and tracking — deployed on cloud infrastructure or directly to edge hardware depending on your latency needs.
0%
Avg. Detection Accuracy
<100ms
Inference Latency
8-12 wks
Typical Engagement
0+
Models Shipped to Edge
What's Included
Trained vision model with benchmark report
Inference API or edge deployment package
Labeled dataset and annotation guidelines
How We Work
01
Data Collection Audit
We assess your existing image/video data and identify gaps that need labeling or collection.
02
Model Selection & Training
We train or fine-tune a vision model suited to your accuracy and latency requirements.
03
Pipeline Engineering
The model is wrapped in an inference pipeline that handles real camera or upload streams.
04
Deployment & Monitoring
We deploy to cloud or edge and monitor accuracy drift as conditions change.
What You'll Receive
- Trained vision model with benchmark report
- Inference API or edge deployment package
- Labeled dataset and annotation guidelines
- Monitoring dashboard for accuracy drift
- Integration documentation
How Engagements Typically Work
Every project starts with scoping — here's the shape most engagements for this service take.
Proof of Concept
A 2-3 week sprint training on a sample of your real images to validate feasibility before full build.
Production Build
Full pipeline development from data labeling through deployment — typically 8-12 weeks.
Ongoing MLOps
Monthly retainer for retraining as camera conditions, angles, or object classes change.
Technologies We Use
The tools and platforms our Computer Vision team works in day to day.
Vision Frameworks
Deployment
Frequently Asked Questions
Often no — pretrained vision models can be fine-tuned on a few hundred well-chosen examples, and we help identify what to label first.
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