At Dysnix, we believe in leveraging technology to create meaningful social impact and enhance human life. Our collaboration with Explorer Surgical, now a part of GHX, exemplifies this commitment by building a secure and scalable AI platform that directly contributes to improving surgical outcomes and advancing healthcare delivery.
This project underscores our dedication to supporting innovations that make a tangible difference in critical fields.
At the moment the client addressed us, Explorer Surgical was a startup delivering a digital case support platform that connected suppliers with healthcare provider teams.
Founded in 2015, Explorer Surgical was developed at The University of Chicago Medical Center by Jennifer Fried and Dr. Alex Langerman. Backed by Elliott Management, Aphelion Capital, and the University of Chicago, it serves healthcare suppliers across multiple therapeutic areas.
The platform empowers surgical teams to collaborate in real time, standardize best practices, and drive improved patient outcomes through data-driven insights and AI-powered guidance.
Explorer Surgical’s growth trajectory brought a set of technical and operational challenges:
Explorer Surgical engaged Dysnix for their expertise in cloud-native architecture, DevSecOps, and secure AI infrastructure. Dysnix’s experience with healthcare and AI startups, and their ability to deliver compliant, scalable solutions, aligned with Explorer Surgical’s vision for a resilient and innovative platform.
Our team was highly qualified to help the client at both the Proof of Concept (PoC) stage and during the implementation of complex DevSecOps practices.
At the PoC stage, we assembled a dataset, trained a model for detecting surgical instruments, and developed an application utilizing this model. At that stage, the application was able to:
Dysnix migrated Explorer Surgical’s workloads to a Kubernetes-based architecture on AWS. This enabled:
A robust DevSecOps pipeline was established to streamline delivery and embed security at every stage:
To support AI-driven features, Dysnix:
Given the regulatory landscape, Dysnix implemented:
From a DevOps and MLOps perspective, this stack is engineered for reliability, scalability, and rapid iteration.
Kubernetes orchestrates containerized services, while Helm and Terraform automate deployment and infrastructure management, ensuring consistency and traceability. AWS provides the backbone for compute, storage, and managed services, with EKS running scalable clusters and EC2 supporting GPU workloads for deep learning.
For AI and data workflows, PyTorch powers model development, with InceptionNet and ResNet architectures supporting advanced computer vision tasks. OpenCV and LabelMe facilitate image processing and annotation, while MLflow manages experiment tracking and model lifecycle. Scikit-learn, Numpy, and Pandas enable robust data analysis and preprocessing, with Pillow and Matplotlib supporting image manipulation and visualization.
CI/CD pipelines (GitHub Actions) automate testing, security checks, and deployment, while Prometheus, Grafana, and CloudWatch provide comprehensive monitoring and alerting. Security is enforced at every layer, from IAM and VPC segmentation to encryption and automated compliance checks, supporting HIPAA and internal standards.
For the team of 3 engineers and 4 months of project duration, Dysnix reached all the primary goals, SLOs, and most viable performance indicators:
With a secure, scalable, and compliant platform in place, Explorer Surgical—now part of GHX—continues to advance digital and AI-driven collaboration in healthcare. The foundation built with Dysnix enables ongoing innovation, rapid feature delivery, and operational excellence as part of GHX’s broader mission to drive value-based care.
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