HOPPR Analysis: $32M Raised
What is HOPPR?
Secure platform for medical imaging AI development
Employees
11-50
Founded
2019
Valuation
$31.5M
Latest Funding Round Size
$31.5M
Product Features & Capabilities
- Secure AI development environment
- Foundation models for medical imaging
- Fine-tuning tooling
- Quality Management System (QMS)
- Data management platform with known provenance
Use Cases
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
How much HOPPR raised
Funding Round - $31.5M
RecentGtm Strategy
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.
Reported Clients
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.
Tech Stack 1
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:
- Programming Languages: Python (for API development) and React (for frontend applications).
- Cloud Platforms: AWS (Amazon Web Services) for cloud infrastructure.
- Programming Languages: Python.
- Deep Learning Frameworks: PyTorch and TensorFlow.
- Data Manipulation Tools: SQL, pandas, and NumPy.
- Machine Learning Operations: Familiarity with ML Ops practices is preferred.
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.
