Ryght
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About
Ryght is an advanced generative AI platform specifically engineered to modernize the site selection and feasibility phases of clinical trials. In an industry where sponsors and Contract Research Organizations (CROs) often rely on fragmented spreadsheets and manual data entry, Ryght introduces a centralized, data-driven ecosystem. The platform's primary purpose is to accelerate the transition from protocol design to patient enrollment by providing high-fidelity insights into research site capabilities worldwide. By leveraging artificial intelligence, the tool aims to eliminate the guesswork and delays traditionally associated with global trial setup, ultimately helping biopharma companies bring life-saving treatments to market faster. The platform’s technological foundation is built upon AI Site Twins, which are comprehensive digital replicas of clinical research sites. These virtual models capture a site’s historical performance, operational infrastructure, and local patient demographics. In practice, users utilize the Network Navigator to upload protocol synopses, which the AI then analyzes to identify the best-fit locations. Beyond simple matching, Ryght enables teams to simulate and forecast enrollment performance, allowing them to anticipate potential bottlenecks before they occur. The Feasibility Accelerator further streamlines the workflow by automating the distribution of pre-populated questionnaires, while retriever agents continuously scan public and proprietary sources to ensure site data remains current and interactive. Ryght is best suited for clinical operations professionals, feasibility managers, and data scientists within biopharmaceutical companies and CROs. It is particularly valuable for those managing large-scale or complex trials in specialized fields like oncology and hematology, where finding the right patient population is critical. What distinguishes Ryght from traditional site databases is its predictive nature; rather than offering a static list of facilities, it provides a dynamic simulation environment. Furthermore, its availability on the Microsoft Azure Marketplace and its Enterprise Agent Architect allow for deep customization and seamless integration into existing enterprise cloud infrastructures, making it a robust choice for large-scale research organizations.
Pros & Cons
Creates predictive digital replicas of research sites to simulate enrollment performance.
Automates site outreach with pre-populated questionnaires to accelerate feasibility assessments.
Maintains real-time data accuracy using autonomous retriever agents to monitor global sites.
Integrates with Microsoft Azure for simplified enterprise billing and procurement processes.
Provides a free proof-of-concept by identifying sites for a live protocol synopsis.
Specific subscription pricing is not publicly listed and requires a custom quote.
Full implementation of custom agents requires direct engagement with their engineering team.
Site performance predictions rely on the depth of historical data available in the network.
Use Cases
Clinical Operations Managers can use the Network Navigator to instantly match complex study protocols with sites that have relevant patient demographics.
Feasibility Teams can leverage automated questionnaires to reduce the administrative burden of site vetting and speed up activation timelines.
Biopharma Executives can utilize enrollment forecasting to predict trial success rates and optimize site selection strategies before investment.
Data Scientists can use the Enterprise Agent Architect to build custom, industry-specific AI models for specialized research needs.
CROs can integrate Ryght through Azure Marketplace to streamline their technology stack and leverage existing cloud commitments for new trials.
Platform
Task
Features
• azure marketplace deployment
• ai retriever agents
• protocol analysis
• enrollment forecasting
• enterprise agent architect
• feasibility accelerator
• network navigator
• ai site twins
FAQs
What are AI Site Twins?
AI Site Twins are digital replicas of every clinical research site, capturing operational characteristics, historical performance, and patient populations. They allow sponsors to run predictive simulations to forecast enrollment and identify potential issues before a trial begins.
How does the Feasibility Accelerator streamline workflows?
The tool automatically sends pre-populated feasibility questionnaires to qualified sites identified by the AI. This eliminates manual data entry for sites and allows sponsors to track and compare all responses in a centralized dashboard.
Can Ryght be integrated with existing cloud services?
Yes, Ryght is available on the Microsoft Azure Marketplace, allowing sponsors to utilize existing cloud commitments. It also partners with major technology providers like AWS and Databricks for enterprise-level scalability.
What is the purpose of the Enterprise Agent Architect?
This feature allows Ryght to design and deploy custom AI agents tailored to an organization's specific research requirements. These agents can range from turnkey solutions to highly specialized tools for unique clinical challenges.
Is there a way to verify the tool's accuracy before purchasing?
Ryght offers a no-cost trial where sponsors can submit a protocol synopsis. The platform will use its AI Site Twins to identify and engage best-fit sites to prove the effectiveness of the matching technology.
Pricing Plans
Enterprise
Unknown Price• Full Network Navigator access
• Feasibility Accelerator automation
• Custom Enterprise Agent Architect
• Azure Marketplace integration
• Real-time enrollment forecasting
• Retriever agent data updates
Free Protocol Test
Free Plan• Free site identification for one protocol
• Protocol synopsis analysis
• Engagement of best-fit sites
• Proof of concept demonstration
• No commitment required
Job Opportunities
DevOps Engineer
Optimize clinical trial site selection and feasibility using AI digital twins to match study protocols with high-performance research sites and patient populations.
Benefits:
Competitive salary
Comprehensive benefits package
Opportunity to work with cutting-edge technology including AI/ML initiatives
Professional development opportunities and conference attendance
Flexible work arrangements and strong work-life balance
Experience Requirements:
5+ years DevOps, Cloud, or SRE experience in production environments
Hands-on expertise with Azure, Kubernetes, Docker, and CI/CD automation
Strong knowledge of networking, security, load balancing, and secrets management
Strong scripting and automation skills (Bash, Python, or similar)
Experience supporting AI/ML infrastructure and model deployment workflows
Other Requirements:
Familiarity with IaC (Terraform or Azure Bicep)
Experience with projects involving high speed data processing, large databases, performance
Prior experience supporting distributed offshore engineering teams
Experience supporting compliance requirements (HIPAA, GxP, ISO, or similar)
Knowledge of regulated healthcare or life sciences industries
Responsibilities:
Manage and optimize Azure-based environments (compute, networking, security, storage, Kubernetes)
Architect and maintain CI/CD pipelines (GitHub Actions preferred)
Own monitoring, logging, and alerting for proactive issue resolution
Partner with compliance teams to implement required controls for clinical data environments
Deliver tooling and automation that reduce friction for developers
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