O&P Cloud

How the AI Factory Works

Every Stage of Building Proprietary Clinical AI

The AI Factory isn't a single training step — it's the full infrastructure for turning raw clinical data into a deployed, production-ready model. Here's how it works end to end.

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The AI Development Lifecycle

Every Stage. Fully Supported.

From raw clinical data to production-ready AI — the AI Factory supports every step.

  1. 1

    Clinical Data

    Organize and version your raw clinical dataset from patient scans and outcomes.

  2. 2

    Organize & Version

    Structure data for reproducible training runs and experiment tracking.

  3. 3

    Align & Normalize

    Standardize geometry orientation and metadata across your dataset.

  4. 4

    Clean & Label

    Prepare ground truth training data with clinical labeling tools.

  5. 5

    Build Training Pairs

    Pair input and expert-produced output meshes into training examples.

  6. 6

    Train

    Run training experiments with full hyperparameter tracking and model registry.

  7. 7

    Validate

    Clinical validation workflows, benchmark testing, and human review.

  8. 8

    Deploy

    Deploy your trained model where it works best for your organization. Run inference on Cloud CAD's platform and call it directly from your enterprise applications, or export and deploy to your own servers for fully self-hosted, proprietary usage — your model, your infrastructure, your control.

  9. 9

    Monitor & Improve

    Continuous performance monitoring and model improvement pipeline.

Platform Capabilities

Complete AI Development Infrastructure

Five capability areas covering the full AI development lifecycle.

Data Management

  • Clinical data organization
  • Dataset versioning
  • Patient anonymization
  • Metadata management
  • Outcome tracking

Data Preparation

  • Scan alignment
  • Mesh normalization
  • Labeling tools
  • Ground truth creation
  • Dataset quality management

Model Development

  • Training infrastructure
  • Experiment tracking
  • Hyperparameter tuning
  • Model registry
  • Version management

Validation

  • Clinical validation workflows
  • Benchmark testing
  • Performance analysis
  • Human review
  • Approval workflows

Deployment

  • Deploy to Cloud CAD — call from your enterprise apps
  • Self-host on your own servers for full proprietary control
  • Local or cloud inference
  • Model versioning & rollback
  • Continuous improvement pipeline

See the AI Factory Live

Talk to our team about applying this lifecycle to your own clinical data.

Talk to Our Team