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Secure platform for medical imaging AI development
Develop AI imaging applications for radiology; Refine imaging models with traceable records; Utilize proprietary datasets for model training; Ensure compliance for regulatory submissions; Optimize AI models for specific diagnostic challenges
HOPPR employs a sales-led growth strategy. Their website analysis reveals a focus on structured sales processes, with an emphasis on contacting sales for product access rather than self-service options. The lack of transparent pricing and the nature of customer testimonials suggest a model oriented towards enterprise relationships. The educational resources provided support this approach, indicating a commitment to high-touch interactions rather than rapid user adoption typical of product-led growth.
HOPPR has notable partnerships with Viz.ai and RadNet's DeepHealth. The collaboration with Viz.ai involves a five-year partnership to integrate HOPPR's medical-grade AI foundation model with Viz.ai's care coordination tools, focusing on projects like detecting pulmonary fibrosis in chest x-rays and screening lung CT imaging studies for lung nodules. The partnership with RadNet's DeepHealth aims to create fine-tuned models using HOPPR's foundation model to enhance DeepHealth's AI-powered health informatics portfolio, improving diagnostic accuracy and efficiency in radiology.
HOPPR utilizes a variety of technologies and tools across different roles, primarily in engineering and data science.
In the Software Engineer role, the technologies mentioned include:
For the Data Scientist position, the following technologies are highlighted:
The Machine Learning Engineer role did not provide specific technology details in the available data.
Overall, HOPPR's technology ecosystem reflects a strong emphasis on Python and cloud services, particularly AWS, along with various data manipulation and deep learning frameworks in their data science roles.