Unlock the Future of Computer Vision
Train, deploy, and manage object detection models—no coding required.
Computer vision shouldn't require a PhD
Labeling is slow and manual
Teams spend weeks annotating thousands of images by hand, delaying model deployment.
Models break when data changes
New environments or lighting conditions require complete retraining from scratch.
Deploying CV systems takes weeks or months
Complex infrastructure and dependencies make production deployment a bottleneck.
What IRIS does differently
Human-in-the-loop labeling
Accelerate annotation with AI-assisted labeling and validation workflows.
Automatic similarity discovery
Find and label similar objects across your dataset with one click.
Multi-model training
Experiment with YOLO, RetinaNet, Faster R-CNN, and more—all in one platform.
Continuous iteration
Retrain, compare, and deploy models without complex pipelines or code.
How IRIS Works
From first label to deployed model in minutes
Label What Matters
Users define the objects that matter with fast, intuitive labeling.
Who IRIS is for
Product & Ops Teams
Build and deploy computer vision features without hiring ML engineers.
Computer Vision / ML Engineers
Accelerate experimentation and focus on model performance, not infrastructure.
Security & Defense Programs
Deploy mission-critical CV systems with private cloud and air-gapped support.
Manufacturing & Industrial Teams
Implement quality control and safety monitoring without production delays.
Enterprise & Government Teams
IRIS supports private deployments, custom workflows, and mission-specific configurations.
Request Beta Access
Tell us about yourself and your use case to get priority access.
Frequently Asked Questions
Yes. IRIS provides a fully visual interface for labeling data, validating detections, configuring training, and deploying models. You can experiment with multiple architectures and training configurations without writing any code—while still retaining fine-grained control when you need it.