Clinical trial site selection reimagined: PSI CRO deploys Arango-powered SYNETIC to eliminate $30,000 site failures

PSI CRO, a privately held global full-service contract research organization headquartered in Zug, Switzerland, has deployed an AI-powered site selection engine called SYNETIC that compresses what was a six-week manual identification process into minutes. The system runs on the Arango Contextual Data Platform, a multimodel data infrastructure developed by San Jose-based Arango, and was unveiled publicly at NVIDIA GTC on 19 March 2026. The operational improvement directly targets one of clinical research’s most expensive structural inefficiencies: the persistent failure of trial sites to enroll patients, a problem that routinely adds millions of dollars to the cost of a single study. For pharmaceutical and biotechnology sponsors who depend on CROs to execute pivotal trials on schedule, the announcement signals a meaningful shift in how site intelligence can be industrialized at scale.

Why do clinical trials lose millions to underperforming sites and how does AI change that calculation?

The structural economics of clinical trial site management have long been unfavorable. Activating a single trial site can cost upward of $30,000, and large multi-country studies routinely involve hundreds of sites simultaneously. Against that backdrop, the industry’s persistent enrollment failure rate represents a significant misallocation of capital. According to data cited in connection with the SYNETIC launch, somewhere between 30 and 40 percent of clinical trial sites under-enroll relative to projections, and approximately 11 to 15 percent never recruit a single patient across the full duration of a study. For a phase three oncology program enrolling across 40 or more countries, even a modest reduction in the proportion of non-enrolling sites can recover millions in activation costs and prevent month-long timeline slippage that cascades through regulatory filing schedules.

The core difficulty is not a shortage of data. PSI CRO has accumulated 30 years of operational history spanning more than 500,000 institutions, three million study sites, and 300,000 clinical projects. The problem is that this information has historically existed in fragmented form across multiple internal systems, making it difficult for feasibility teams to query across investigators, institutions, historical enrollment rates, therapeutic expertise, and protocol-specific complexity at the same time. The result has been a labor-intensive process where compiling a defensible shortlist of candidate sites could take experienced teams up to six weeks, relying on a combination of institutional memory, manual database queries, and bilateral outreach.

How does the Arango Contextual Data Platform unify fragmented clinical research data for AI-driven site ranking?

Arango’s platform is positioned as a natively multimodel contextual data layer, combining graph relationships, vector embeddings, document storage, key-value data, and search functionality within a single architecture rather than requiring organizations to stitch together separate point solutions for each data type. For PSI CRO, this meant consolidating its historical trial data, investigator profiles, institutional capabilities, patient population metadata, and study outcomes into a unified knowledge base that SYNETIC can query in real time. The graph layer preserves the relational structure of the data, capturing how specific investigators are connected to institutions, how those institutions have performed in analogous therapeutic areas, and how historical enrollment rates correlate with protocol design variables.

The SYNETIC platform then applies what PSI describes as an OmniRAG approach, combining retrieval-augmented generation with advanced reasoning from large language models to analyze protocol requirements against the knowledge base and produce a ranked list of candidate sites. Each recommendation includes a rationale drawn from historical trial data, a confidence score, and visibility into where information gaps exist. This explainability layer is not incidental. Clinical research operates in a regulated environment where decisions must be defensible to sponsors, ethics committees, and regulatory agencies. An AI system that produces recommendations without transparent reasoning would face immediate credibility barriers regardless of its predictive accuracy.

What does explainable AI actually mean in a regulated clinical trial environment and why does it matter for sponsors?

The concept of explainability in AI has been debated extensively in enterprise contexts, but clinical research introduces a harder version of the problem. When a sponsor’s medical monitor or a regulatory inspector asks why a particular site was included or excluded from a pivotal trial, the answer cannot be “the model said so.” PSI CRO has addressed this directly in the SYNETIC architecture by requiring the system to surface supporting evidence alongside each recommendation, including which historical studies informed the scoring, what protocol-specific factors weighted the outcome, and where the underlying data is sparse or conflicting.

This approach reflects a broader principle that is gaining traction among clinical operations teams: AI tools deployed in regulated healthcare workflows must be auditable, not just accurate. Confidence levels and knowledge-gap flags serve a dual purpose, helping feasibility teams prioritize verification efforts and providing a structured audit trail if site selection decisions are later questioned. For PSI CRO’s sponsor clients, particularly those running late-stage oncology or rare disease programs where a single study can carry a multi-billion-dollar regulatory submission, the ability to document how site recommendations were generated carries material risk management value.

How does PSI CRO’s SYNETIC platform compare to what other major CROs are building in AI-driven site selection?

PSI CRO is not the only contract research organization investing in AI-augmented site intelligence. The largest players in the CRO market, including IQVIA, Covance (a division of Labcorp), PPD (now part of Thermo Fisher Scientific), and ICON, have each developed proprietary data platforms and predictive analytics capabilities built on years of accumulated trial data. IQVIA in particular has invested heavily in real-world data assets and analytics infrastructure that inform feasibility work. What differentiates PSI CRO’s approach is the architectural decision to build SYNETIC on a dedicated contextual data platform rather than layering AI inference on top of a conventional relational or data warehouse stack.

For mid-sized CROs, the competitive calculus around technology investment is more acute. PSI CRO employs approximately 3,200 professionals across 56 countries and specializes in complex phase two and phase three trials in oncology, hematology, immunology, neurology, and other high-stakes indications. It cannot match the raw data volume of IQVIA or Labcorp, which means differentiation must come from the density of insight extracted per data point rather than from data breadth alone. By unifying its 30 years of operational history into a continuously learning graph-based knowledge system, PSI CRO is effectively compressing its institutional expertise into a queryable format that can scale without proportional headcount increases in feasibility operations.

What are the execution risks and limitations of deploying agentic AI in high-stakes clinical trial site selection?

The transition from weeks-long manual processes to AI-generated recommendations in minutes is a compelling efficiency story, but it carries real execution risks that the industry will watch closely. Historical performance data is inherently retrospective, and site selection quality depends on how well the model generalizes to novel protocol designs, emerging therapeutic modalities, and geographies where PSI CRO has limited prior presence. Rare disease programs targeting small patient populations, for instance, may present protocol variables with insufficient historical precedent to generate confident AI-driven scores, precisely the scenarios where manual expert judgment has historically been most valuable.

There is also a dependency risk embedded in the Arango platform relationship. PSI CRO has built a core operational capability on a third-party data infrastructure vendor. Arango is a privately held company with a focused product mandate and a growing enterprise client base that includes the London Stock Exchange, Siemens, the US Air Force, and the National Institutes of Health. Its commercial viability appears solid, but any CRO outsourcing a fundamental competitive differentiator to an external platform must manage vendor concentration risk carefully, particularly as the contractual research market consolidates and competitive intelligence around proprietary systems becomes strategically sensitive.

What does the Arango and PSI CRO collaboration signal about the broader shift to contextual data infrastructure for enterprise AI agents?

The SYNETIC deployment reflects a pattern that is becoming visible across industries attempting to operationalize AI agents beyond controlled proof-of-concept environments. The failure mode is rarely the model itself. Large language models are sufficiently capable for a wide range of analytical and reasoning tasks. The bottleneck is the data layer: fragmented, inconsistently structured, historically siloed enterprise data that lacks the relational richness required for agents to reason across complex domains. Graph databases and multimodel data platforms have been positioned as a solution to this problem for several years, but the clinical research case study provides a high-stakes, measurable illustration of what contextual data infrastructure actually enables in production.

Arango’s platform is part of a broader competitive landscape that includes Neo4j in graph databases, Microsoft’s investment in fabric-based data unification, and specialist RAG infrastructure vendors building retrieval layers for enterprise LLM applications. The announcement at NVIDIA GTC is itself strategic positioning: Arango is a member of the NVIDIA Inception Program, and presenting alongside PSI CRO at one of the year’s most prominent AI infrastructure events places the Arango Contextual Data Platform in front of an audience actively evaluating production AI deployments across healthcare, defense, and financial services. For organizations that have been stuck at the pilot stage with AI agents, the PSI CRO case provides a concrete reference architecture worth examining closely.

Key takeaways: What the PSI CRO and Arango SYNETIC platform means for clinical research AI and CRO competitiveness

• PSI CRO has reduced clinical trial site identification from up to six weeks to minutes using SYNETIC, an AI knowledge engine built on the Arango Contextual Data Platform, announced at NVIDIA GTC on 19 March 2026.
• The SYNETIC system draws on 30 years of PSI operational history covering more than 500,000 institutions, three million study sites, and 300,000 clinical projects, unified into a single multimodel contextual data layer.
• The platform addresses a persistent structural inefficiency in clinical research: 30 to 40 percent of trial sites under-enroll and roughly 11 to 15 percent never enroll a single patient, translating to millions in wasted site activation costs per study.
• Explainability is built into the architecture, with SYNETIC surfacing supporting evidence, confidence scores, and knowledge gap flags alongside every site recommendation, meeting the auditability demands of regulated healthcare environments.
• For mid-sized CROs competing against larger players with greater data volume, the ability to extract denser insight from existing historical data through graph-based contextual AI represents a meaningful capability differentiator.
• The Arango Contextual Data Platform’s natively multimodel architecture, combining graph, vector, document, key-value, and search in a single system, avoids the fragmented technology stacks that typically degrade enterprise AI agent performance.
• Execution risks include the retrospective nature of historical performance data, potential limitations in novel therapeutic areas or underrepresented geographies, and vendor concentration risk given the strategic dependency on a third-party platform.
• The PSI CRO deployment provides a high-visibility reference architecture for enterprise AI agents that are blocked not by model capability but by fragmented contextual data infrastructure, a pattern visible across healthcare, financial services, and defense.
• Arango’s growing client roster, which includes the London Stock Exchange, Siemens, the US Air Force, and the NIH alongside PSI CRO, positions the platform as enterprise-grade infrastructure rather than a niche specialist tool.
• For pharmaceutical and biotechnology sponsors evaluating CROs for complex late-stage programs, the ability of a CRO to document and explain AI-driven site selection decisions will increasingly become part of the operational due diligence conversation.