Build the image AI your business needs. Own it.
S4IR gives engineering teams a platform to design multi-stage image analysis pipelines, train models on their own data, and deploy at scale - without building the infrastructure or hiring ML specialists.
A pipeline, not just a model
Real image analysis tasks need multiple models working in sequence - detect the object, isolate the parts, classify each one, flag the edge cases. S4IR lets you design that pipeline stage by stage, each with its own model trained on your data.
Your data builds your IP
Every model you train on S4IR is trained on your images, your labels, your domain knowledge. The weights are yours, stored in your infrastructure. You are not renting a generic API - you are building AI that reflects your specific product and process.
Accurate, scalable, and improving
With enough labeled data, models trained on S4IR reach production-grade accuracy - 99% is achievable for well-defined tasks. For teams still building their dataset, the platform helps collect the right images and labels to get there faster.
How it works
A single platform that covers the full lifecycle - from pipeline design to continuous improvement.
Design your pipeline
Define the stages your image analysis requires. Each stage has its own model, class labels, and routing logic. Configure detection, classification, condition grading, or any combination - without writing infrastructure code.
Train on your images
Upload your images, build your dataset, and train. The platform guides model selection, training strategy, and result interpretation - so your engineering team can build accurate models without ML expertise on staff.
Deploy, measure, and improve
Run the pipeline via API or web UI. Every result comes back with a confidence score. Low-confidence cases are routed for human review or to an LLM vision model for edge cases. Correct predictions, retrain, promote - production stays stable throughout.
Built for production
Not a prototype. Not a custom project. A platform your engineering team can operate, improve, and own over time.
Multi-stage pipeline
Chain detection, classification, and condition models in sequence. Each stage feeds the next - outputs from object detection become inputs for part classification, and so on.
Multi-tenant isolation
Each client operates in a fully isolated environment. Images, models, pipeline config, and API keys are never shared across tenants.
Confidence scores and smart routing
Every result includes a confidence score. Route low-confidence cases automatically - to a human review queue, or to an LLM vision model for ambiguous edge cases.
Model versioning and rollback
Retrain and promote new model versions without touching production. If a new version underperforms, roll back instantly.
Full audit trail
Every prediction is logged with its inputs, outputs, model version, and confidence score. Full traceability for compliance and QA.
On-premise deployment
The platform runs inside your infrastructure. Images, model weights, and predictions never leave your server. Air-gap compatible for regulated environments.
Still analyzing images manually - or patching together generic APIs?
Talk to us about your workflow. We'll show you what a pipeline built on your own data looks like.