Solutions
One platform. Every stage of your image analysis pipeline, configurable per client. S4IR replaces manual image review, generic APIs, and failed in-house builds with a platform where engineering teams design the pipeline, train the models, and own the result.
Platform capabilities
One platform core. Every capability configurable per client, with full data isolation.
Multi-stage pipeline builder
Design an ordered sequence of analysis stages. Each stage has its own model, class labels, and routing logic - object detection feeds into part classification, which feeds into condition grading. All configurable without infrastructure work.
Model training on your images
Upload your images, build labeled datasets, and train. The platform guides model selection and training strategy so your team does not need ML expertise to get to production accuracy.
Confidence scores on every result
Every output includes a confidence score. Set thresholds to route low-confidence results to human review - or to an LLM vision model for ambiguous edge cases.
Smart routing
Route pipeline outputs to different destinations - queues, webhooks, downstream systems - based on predicted class, confidence level, or any combination of conditions.
Dataset management
Build and version labeled datasets per stage, per tenant. Correct predictions from production and feed them directly back into your training data.
Model versioning and rollback
Retrain and promote new versions independently for each pipeline stage. If a new version underperforms, roll back instantly - production is never at risk.
LLM vision integration
For edge cases that fall below confidence thresholds, the platform routes to an LLM vision model automatically. Powerful for ambiguous inputs, without paying LLM costs on every item.
REST API
Every capability is available via API - submit images, retrieve results, upload training data, trigger retraining, manage model versions, configure routing. Integrate into any existing system.
How the platform works
The same lifecycle for every client - design, train, improve.
Design the pipeline
Define the stages your workflow requires. Assign models, set class labels, configure routing rules. Each client works in a fully isolated tenant workspace.
Train and deploy
Upload images, build your dataset, train models for each stage. The platform guides the process. When a model is ready, promote it to production - each stage independently.
Improve continuously
Review predictions, correct errors, retrain. New versions go live only when you promote them. Accuracy improves as your dataset grows - production stays stable throughout.
Use cases
Any workflow where images are currently reviewed manually - or routed through generic APIs that do not fit the domain - is a candidate for S4IR.
Mobile device condition grading
A device reseller builds a multi-stage pipeline: detect the device, identify front or rear, classify screen condition, flag physical damage. Each stage is a model trained on their own device images. Consistent grading at intake volume, with edge cases routed for human review.
Manufacturing defect detection
A factory replaces end-of-line visual inspection with a pipeline: detect components, classify each one, identify defect type. Defective items are routed to the correct rework queue automatically - the same way, every shift, with a full audit trail.
Industrial parts identification
A parts distributor builds a pipeline to identify components from images - category, manufacturer, condition grade. Models trained on their own catalog. New parts are flagged for human labeling and fed back into the dataset to improve accuracy over time.
Have an image analysis workflow in mind?
Tell us about your process and we'll show you how to model it as a pipeline.