CARPL.ai
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About
CARPL.ai is a comprehensive enterprise imaging platform designed to simplify the adoption of artificial intelligence in radiology. Acting as a centralized hub, it allows healthcare providers to navigate a massive marketplace of over 250 AI applications from 85 different vendors. The platform addresses the fragmentation in the medical imaging market by providing a single technical integration pipeline, which enables facilities to run multiple AI models—for everything from fracture detection to mammography—through a unified user interface. It is FDA 510(k) cleared, ensuring it meets rigorous regulatory standards for clinical use while providing a standardized environment for multi-vendor orchestration across the healthcare enterprise. The platform operates through a structured four-stage workflow: Discover, Explore, Validate, and Deploy. Users first identify relevant AI tools from the global marketplace and then explore their potential by running them against their own clinical data. A key differentiator is the validation module, which allows radiologists and data scientists to perform deep clinical and statistical analysis, generating performance summaries and key metrics like AUC (Area Under the Curve) to determine a tool's actual value before purchase. Once selected, the AI is deployed via a single technical integration that works with existing PACS and RIS infrastructure, and the Universal AI Viewer allows clinicians to view, edit, and annotate results within their daily workspace. CARPL.ai is primarily built for radiologists, hospital IT departments, and AI developers. Radiologists benefit from increased productivity and better patient outcomes, as seen in institutional programs like lung cancer screening or thyroid assessments. IT departments find value in the platform’s infrastructure-agnostic nature, allowing for both on-premise and cloud deployments without the headache of managing dozens of individual vendor security reviews and APIs. For enterprise partners and developers, it serves as a critical testing and monitoring ground to ensure AI safety and performance over time, providing transparency that many individual point solutions currently lack in the marketplace. What sets the platform apart is its "single-access-point" philosophy combined with a heavy emphasis on clinical validation and longitudinal monitoring. Rather than just acting as a digital storefront for AI, it provides a robust testing environment to combat "black box" AI concerns. By allowing users to compare multiple algorithms side-by-side on their own specific patient demographics, the platform ensures that healthcare providers make data-driven purchasing decisions. Its deep integration with established PACS vendors like Philips and Fujifilm further cements its role as a necessary bridge between cutting-edge AI innovation and practical, high-volume clinical workflows.
Pros & Cons
Access to the largest marketplace with over 250 radiology AI applications from 85+ vendors.
Simplifies IT management by providing a single integration point for all AI needs.
Enables deep clinical and statistical validation using your own institutional data.
FDA 510(k) clearance provides confidence in the platform's clinical and technical safety.
Supports a wide range of deployment environments including on-premise and multiple cloud vendors.
Specific pricing for applications and the platform is not publicly listed and requires a demo.
Full clinical ROI calculation requires significant testing using the built-in validation module.
Initial orchestration of multiple vendors may require specialized setup from clinical solutions architects.
Use Cases
Radiologists can view and edit results from multiple different AI vendors through a single, standardized universal viewer.
IT Departments can reduce technical debt by managing dozens of AI applications through one technical integration and API.
Clinical Researchers can use the validation module to generate performance summaries and AUC metrics for academic or safety studies.
Hospital Executives can run pilots on internal data to compare different AI algorithms and make data-driven purchasing decisions.
AI Developers can monitor the real-world clinical performance and safety of their models after deployment in hospital environments.
Platform
Features
• fda 510(k) cleared
• multi-vendor orchestration
• performance monitoring tools
• infrastructure agnostic
• single integration pipeline
• clinical validation module
• universal ai viewer
• 250+ ai marketplace
FAQs
How many AI applications are available on CARPL.ai?
The platform features a marketplace of over 250 AI applications from more than 85 vendors, covering clinical areas like mammography, stroke, and chest imaging.
Is the CARPL.ai platform regulatory compliant?
Yes, the platform is FDA 510(k) cleared, ensuring it meets the safety and effectiveness standards required for an enterprise imaging AI platform.
Can I test AI tools on my own data before purchasing?
Yes, the platform's 'Explore' and 'Validate' modules allow you to run AI applications on your own clinical data to perform deep statistical analysis and assess value.
What types of deployment options are available?
CARPL.ai is infrastructure agnostic and can be deployed on-premise or in the cloud with any major cloud vendor depending on the facility's requirements.
How does it integrate with existing hospital systems?
The platform uses a single technical integration pipeline to connect multiple AI systems to your existing PACS, RIS, and clinical workflows through flexible APIs.
Pricing Plans
Enterprise
Unknown Price• Access to 250+ AI applications
• Single technical integration pipeline
• Universal AI Viewer
• Clinical validation module
• Statistical performance metrics
• Real-time AI monitoring
• Infrastructure agnostic deployment
• FDA 510(k) cleared platform
• Support for 85+ AI vendors
• Cloud or on-premise options
Job Opportunities
Sales & Marketing Intern
Access, validate, and deploy over 250 radiology AI applications through a single, FDA-cleared integration platform to enhance diagnostic accuracy and clinical ROI.
Experience Requirements:
Relevant skills and a strong interest in the field
Preferred experience in sales and marketing
Other Requirements:
Available for a full-time, in-office internship
Commit to a 3-month duration
Strong understanding of the LinkedIn platform
Eagerness to learn and gain experience in commercial leadership
Responsibilities:
Conducting thorough market research to gather information on target audiences and competitors
Using LinkedIn to generate leads and compile email addresses
Creating and running marketing campaigns across various channels
Assisting in maintaining and updating the database of leads
Writing and editing emails, brochures, and social media posts
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Graphic Designer
Access, validate, and deploy over 250 radiology AI applications through a single, FDA-cleared integration platform to enhance diagnostic accuracy and clinical ROI.
Education Requirements:
Bachelor’s degree in Design, Visual Communication, Fine Arts, or related field
Experience Requirements:
2–4 years of experience as a Graphic Designer, preferably in a marketing or digital-first environment
Other Requirements:
Proficiency in Adobe Creative Suite (Photoshop, Illustrator, InDesign)
Proficiency in Figma and Canva
Strong portfolio showcasing marketing-related design work
Ability to work on multiple projects with tight deadlines
Responsibilities:
Design creatives for campaigns, including social media posts and digital ads
Maintain and evolve Carpl’s visual identity across all touchpoints
Design product one-pagers, case studies, and explainer graphics
Create web banners, blog graphics, and video thumbnails
Work with growth marketing team to design performance-driven creatives
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Implementation Engineer
Access, validate, and deploy over 250 radiology AI applications through a single, FDA-cleared integration platform to enhance diagnostic accuracy and clinical ROI.
Experience Requirements:
3+ years of IT infrastructure experience working closely with cloud technologies
Other Requirements:
Working knowledge of Docker, Kubernetes, Terraform
Experience with monitoring frameworks like Prometheus, Elastic Stack, Grafana
CICD tools such as Jenkins
Knowledge of Go, Python, and/or Java
Experience with Git, BitBucket, Apache, Nginx
Responsibilities:
Implement scalable, secure, and high-availability infrastructure solutions on AWS
Develop and maintain CI/CD pipelines to accelerate application deployment
Utilize container technologies and orchestration tools like Kubernetes
Implement monitoring tools to proactively identify and resolve performance bottlenecks
Integrate security best practices into the DevOps processes
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