PSI Reduces Clinical Trial Site Identification From Weeks to Minutes Using AI Powered by a Contextual Data Layer
6 weeks → minutes
site identification time
Hundreds of thousands
research projects unified
Millions of dollars
per trial savings potential
Overview
PSI is a leading full-service global Contract Research Organization whose key strength is predictable patient enrollment across multiple therapeutic areas.
PSI is known in the industry as a CRO focused on ‘on-time project delivery’. This is achieved through investing substantial effort and MD resources into performing quality feasibility assessments that provide an accurate predictor of study timelines.
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The Challenge
Selecting the right clinical trial sites is one of the most important – and most expensive – decisions in drug development.
Clinical trials can take 10–15 years to bring a drug to market, and running a trial can cost as much as $160 per minute. Yet the industry faces a major inefficiency: 40–50% of clinical trial sites underperform or never enroll a single patient. Activating a site can cost $30,000 or more, and large trials often involve hundreds of sites. When sites fail to recruit participants, the financial impact can reach millions of dollars per study.
The problem isn’t a lack of data. Clinical research organizations already collect extensive information about investigators, institutions, and past trials.
The real challenge is that critical knowledge is fragmented across multiple systems, making it difficult to analyze how investigators, institutions, protocols, and outcomes relate to one another.
The Solution
PSI built SYNETIC™, an AI-enabled knowledge engine powered by the Arango Contextual Data Platform.
Arango enables PSI to unify structured data, documents, and historical trial information into a single contextual data layer that preserves how investigators, institutions, studies, and outcomes are connected.
By combining graph, vector, document, key value, and search capabilities in one platform, PSI can preserve relationships between:
- investigators and institutions
- study protocols and historical outcomes
- patient populations and recruitment patterns
- standards of care
- regulatory requirements across countries
This unified, current, and trusted business context allows researchers to analyze historical studies, detect patterns, and make faster, more informed decisions.
Our AI agent doesn’t just recommend trial sites — it explains the reasoning, highlights key study factors, and shows confidence levels, all grounded in a unifed, current, and trusted business context.
— Andrei Seryi
Director of Knowledge Management and Process Improvement at PSI
PSI’s AI Agent for Clinical Research
Using Arango, PSI connected hundreds of thousands of historical research projects into a unified contextual data layer bringing together investigators, institutions, protocols, and outcomes that allows AI agents and teams to explore relationships across their entire research history.
Researchers can now quickly identify:
- investigators who perform well under specific protocols
- institutions with access to large pool of target patient population
- patterns that predict successful clinical trial execution
Explainable AI for Clinical Research
In healthcare and life sciences, decisions must be transparent and defensible.
The Arango Contextual Data Platform supports explainable AI by providing:
- clear reasoning behind site recommendations
- evidence drawn from historical trial data
- confidence levels for predictions
- visibility into data gaps or missing information
This transparency allows clinical teams to trust AI insights while maintaining regulatory accountability.
Results
Before Synetic
- Site identification required up to 6 weeks
- Knowledge scattered across multiple systems
- High rate of underperforming sites
With Synetic + Arango
- Site selection reduced to minutes
- Faster identification of high-performing investigators
- Reduction in underperforming institutions
- Millions of dollars saved per clinical trial
Why Arango
The Arango Contextual Data Platform enables organizations to connect fragmented enterprise data across siloed systems while preserving the relationships between people, institutions, processes, and outcomes.
By combining graph relationships, vector embeddings, documents, and search in a single multimodel platform, Arango provides the contextual data foundation needed to power explainable AI and data-driven decision-making.
With this unified, trusted context, PSI unlocked faster insights, explainable AI, and significantly more efficient clinical trial operations. PSI now uses this platform to power AI-driven site selection across its global clinical research operations, helping researchers identify high-performing trial sites faster and avoid costly under-enrolling sites.