HOPPR Analysis: $32M Raised
What is HOPPR?
HOPPR is a platform that connects curated data, models, fine-tuning tools, and regulatory alignment for medical imaging AI applications. It provides a secure environment tailored for the unique demands of healthcare, enabling developers to build and deploy imaging models with traceability and compliance. The platform offers proprietary datasets and foundation models designed for commercial use in radiology and other clinical domains.
Employees
11-50
Founded
2019
Industry
HealthTech, AI/ML, Data Analytics
Valuation
$31.5M
Is Development Tool
Yes
Latest Funding Amount
$31,500,000
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.
For the Data Scientist position, the following technologies are highlighted:
- 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.
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