Ryght AI Secures $3M Seed Funding and Expands Academic Partnerships to Accelerate Clinical Trial Site Selection
Key Insights
Ryght AI (search) raised $3 million in seed funding led by Foothill Ventures (search) to advance its AI-powered clinical trial platform that creates digital twins of over 100,000 research sites worldwide.
The company announced new partnerships with academic medical centers including Emory University, West Virginia University, and West Clinic (search), joining previously established collaborations with USC Keck School of Medicine and Medical College of Wisconsin.
Ryght AI (search)'s platform addresses critical industry inefficiencies where sponsors spend up to $50,000 activating a single site and $50,000-$500,000 enrolling each patient, with 55% of trials failing due to poor site feasibility.
Ryght AI (search) has secured $3 million in seed funding led by Foothill Ventures (search) while announcing strategic partnerships with multiple academic medical centers, positioning the company to transform clinical trial site selection through artificial intelligence. The funding round included participation from existing investors AIX Ventures (search), Virtue Ventures (search), IASO Ventures (search), Top Harvest Capital (search), and Page One Ventures (search).
The company revealed new partnerships with academic medical centers including Emory University, West Virginia University, West Clinic (search), and three additional institutions. These collaborations join previously announced partnerships with The University of Southern California Keck School of Medicine (search) and the Medical College of Wisconsin, demonstrating rapid adoption of Ryght's "service as software" approach in the $140 billion clinical trial services market.
Addressing Critical Industry Inefficiencies
Current clinical trials face significant operational challenges that drive up costs and delay drug development. Sponsors and contract research organizations (CROs) spend up to $50,000 activating a single site and between $50,000-$500,000 enrolling each patient. Despite these substantial investments, 55% of trials fail due to poor site feasibility, slow activation, and mismatches between site capabilities and trial requirements.
"The way that most in the industry identify sites for trial activation and enrollment typically involves downloading spreadsheets of site data from various data vendors with old and inaccurate information, poring over the data to identify investigators and sites that might fit a specific trial, then reaching out to those sites with depressingly low response rates," said Dr. Timothy Sirard, Global Head of Feasibility and Senior Director for Clinical Business Development at QPS (search).
AI-Powered Digital Twin Technology
Ryght AI (search)'s platform addresses these bottlenecks by creating live "digital twin" profiles for over 100,000 clinical research sites worldwide. The technology enables AI-recommended, data-driven site selection, automated feasibility assessments, and rapid activation through a SOC 2-compliant platform.
The platform's key features include:
Site Digital Twin: Virtual, AI-assisted versions of every site that automate workflows for sites, sponsors, and CROs to accelerate site selection and activation. The technology allows sponsors to synthetically test protocol changes and predict downstream impacts on patient enrollment across a global network of actual sites.
Network Navigator: Enables users to query Ryght's network of over 100,000 digital twins to identify and match optimal sites for any study, including new or rescue studies.
Patient Pre-Screening: AI technology that allows sites to rapidly identify eligible patients for open, recruiting studies, eliminating manual work and unnecessary EMR integrations.
Protocol Adherence: Assists sites in managing patient visits and required tests and treatments using AI to ensure compliance with complex protocol requirements, reducing errors and manual labor.
AI Assisted Budgeting: Automatically generates, negotiates, and finalizes site budgets by uploading a trial's schedule of events, compressing weeks of back-and-forth negotiations into minutes.
Partnership with Biorasi Demonstrates Commercial Traction
Ryght AI (search) also announced a strategic partnership with Biorasi (search), a global CRO specializing in dermatology, oncology, neurology, and nephrology studies. The collaboration integrates Ryght's AI-driven platform into Biorasi's operations, providing sponsors with granular, real-time insights on site performance and recruitment capacity.
"Traditional feasibility models can be limited by static, self-reported data," said Chris O'Brien, CEO at Biorasi (search). "Ryght AI (search)'s platform revolutionizes these models. With continuously updated digital twins of clinical sites and AI-powered feasibility automation, Biorasi can now provide sponsors with recruitment forecasts grounded in real-world, site-specific data."
The partnership delivers automated feasibility workflows that compress traditional feasibility timelines from several months to less than three weeks through pre-populated feasibility questionnaire webforms with validated data.
Measurable Impact on Trial Operations
For QPS (search), Ryght AI (search)'s implementation has transformed operational efficiency. "What used to take weeks of back-and-forth can now happen in days, and our teams instead focus on efficiently activating sites that we know are high quality, provide access to the patients and will successfully enroll – across all geographies," Dr. Sirard explained.
Simon Arkell, CEO at Ryght AI (search), emphasized the platform's transformative potential: "We're transforming clinical trial feasibility into reliable and vital study data. Biorasi (search)'s focus on pragmatic AI solutions makes them a perfect partner for optimizing clinical site selection."
The platform's impact extends across multiple operational categories, including enhanced accuracy in enrollment forecasting and budget modeling, reduced delays through intelligent automation and comprehensive site prequalification, cost optimization through decreased risk of mid-trial delays, and improved efficiency through streamlined site engagement targeting high-fit, high-capacity research partners.