Vision that detects, reads, and inspects.
From data labeling to edge deployment, we build vision models that perform on messy, real-world imagery.
Object detection & tracking
Locate and follow objects, people, and products across images and video.
Image classification
Categorize images accurately, even across large, fine-grained label sets.
OCR & document AI
Read text from documents, forms, and scenes, then structure it.
Facial & ID verification
Verify identity and match faces with privacy-aware pipelines.
Video analytics
Understand activity, counts, and events in live or recorded video.
Quality inspection
Spot defects and anomalies on the line faster than the human eye.
Where a camera can do the work.
Quality control
Catch manufacturing defects automatically and consistently.
Retail shelf analytics
Track stock, planograms, and shelf gaps from store imagery.
Security & surveillance
Detect events and anomalies in live video, with alerts.
Medical imaging support
Assist clinicians by flagging regions of interest in scans.
Document digitization
Turn paper and PDFs into structured, searchable data.
Inventory counting
Count and locate items automatically from images.
From pixels to a deployed model.
We build the data pipeline and optimize models to run where you need them, edge or cloud.
Discover & data
Define the vision task, gather imagery, and plan the labeling strategy.
Annotate & model
Label data and select the detection or classification architecture.
Train & optimize
Train, augment, and optimize for accuracy, speed, and model size.
Deploy to edge/cloud
Ship to devices or the cloud with monitoring and retraining pipelines.
Engineered for real-world accuracy.
Proven vision frameworks and optimization tooling so models are accurate and fast enough to run wherever your cameras are.
Vision that works outside the lab.
- Real-world accuracy. We train on messy, representative data so models hold up in production, not just benchmarks.
- Edge-ready. We optimize models to run in real time on-device when latency or privacy demands it.
- Data pipeline included. Labeling, augmentation, and retraining are part of the build, not an afterthought.
- Privacy-aware. We architect facial and personal-data pipelines with privacy and compliance in mind.
Computer vision, answered.
What accuracy can we expect?
It depends on the task and data quality, but we set a target up front and validate against it. For well-scoped problems with good data, 95–99%+ is common.
Do you handle data labeling?
Yes. Labeling strategy and annotation are part of our process. We can label in-house, manage vendors, or use active learning to reduce the effort.
Edge or cloud deployment?
Both. We deploy to the cloud for scale, or optimize models to run on-device (NVIDIA, mobile, embedded) when you need real-time or offline inference.
How much data do we need?
Less than you'd think with transfer learning and augmentation. We'll estimate the dataset size during discovery based on your task.
Can it run in real time?
Yes. We optimize models and pick architectures (like YOLO) that hit real-time frame rates on the hardware you're targeting.
Ready to give your product sight?
Tell us what you need to detect, read, or inspect. We'll scope a vision system to do it reliably.