Embedded Expertise
Direct access to HOPPR’s machine learning and clinical experts.
Our goal is simple:
To help you build medical imaging AI solutions that optimize your workflows faster, securely, and with confidence.
Forward Deployed Engineers (FDEs) complement your team’s strengths through shared problem-solving, transparent collaboration, and solutions designed around your unique clinical and technical needs.
Partner with UsWhy HOPPR® Forward Deployed Services
HOPPR Forward Deployed Services (FDS) brings our experts directly to your team to accelerate AI development in medical imaging.
Accelerated Innovation
Move faster from concept to validated solution.
Workflow-Centric Design
Build solutions grounded in real clinical and operational context.
Transparent Collaboration
True partnership model focused on shared outcomes.
How it Works
Through a hands-on, collaborative model, we help you design, fine-tune, and integrate solutions that align with your data, workflows, and goals.
Define the challenge together
HOPPR’s Forward Deployed Engineer (FDE) collaborates with your team to clarify goals, requirements, and success metrics.
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Things to consider:
- Clinical and/or operational challenge to be solved
- Baseline information on infrastructure and resources
- Project scope and key deliverables
- Milestones and success criteria
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Outputs:
- Requirements to meet solution
- Preliminary timeline
- Performance metrics
- Resourcing and responsibilities
Design and construct the solution
Your team partners with HOPPR experts to implement your solution.
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Things to consider:
- Labeling workflow setup or annotation validation
- Training dataset design
- Fine-tuning and Model optimization
- Preliminary inference pipeline creation
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Outputs:
- Fine-tuned or newly trained model
- Initial inference pipeline
- Model card
- Training metrics + early validation
Embed the model into your workflow
HOPPR FDE’s lead integration planning across technical, IT, and workflow components.
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Things to consider:
- Infrastructure planning
- Security assessment and compliance mapping
- UI/UX alignment
- Draft user acceptance plan
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Outputs:
- Integration plan + architecture diagram
- IT and security approvals
- Dev/test environment setup
- End-user workflow mapping
Confirm the model works in your environment
Clinical and technical validation from your team.
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Things to consider:
- Validation plan for your clinical data
- Human-in-the-loop evaluation with clinicians
- Performance compared to baseline workflows
- Safety review + failure mode analysis
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Outputs:
- Validation packet
- Updated model if needed
- Final acceptance metrics met
- Sign-off from clinical + technical leadership
Launch into routine use
Bring your validated solution into a real-world or research environment.
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Things to consider:
- Deployment timeline for model + inference endpoint
- Monitoring set up
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Outputs:
- Live production deployment
- Monitoring dashboards
- Final launch confirmation
- Sign-off from clinical + technical leadership
Sustain success and model performance
Continuous support to grow and adapt your solution.
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Things to consider:
- Cadence of touchpoints to enable feedback loop
- Metrics to monitor performance, drift, and utilization
- Retrain model if performance or population changes
- Add new features, workflows, or integrations
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Outputs:
- Continuous improvement roadmap
Partner with HOPPR
Let’s build what’s next in imaging AI together.
Work side-by-side with the experts who understand both the technology and the clinical workflow. Tell us a bit about your goals, and our team will connect with you to discuss how we can help.