TrialHub: AI-enhanced data intelligence for clinical trials

Trial protocol design and feasibility assessments demand fast, traceable and scalable research methods, especially when working with fragmented and unstructured clinical datasets. Traditional methodologies and manual synthesis simply can’t keep up with the scale and complexity of aligning patient needs, trial site capabilities and internal R&D timelines.

TrialHub’s pioneering approach to trial planning helps clinical research, pharma and biotech organizations select the best country and sites to meet patient recruitment goals. Powered by Nebius, TrialHub’s data-driven strategy deploys ML capabilities across two primary fronts:

• Short, reliable answers from massive datasets: TrialHub built a question-answering system on a Retrieval-augmented generation (RAG) architecture that delivers immediate access to critical information selected from the most context-relevant sources among millions of medical records. Users can quickly surface the precise answers for their planning needs, from country-specific treatment guidelines to real-world patient insights, across vast volumes of unstructured, dense clinical literature and regulatory frameworks.

• Extracting metrics from text-heavy sources: by deploying advanced Natural language processing (NLP) methods such as semantic search and custom LLMs, TrialHub draws quantitative, comparable insights from medical texts for more informed decision-making. Users can easily assess various countries’ reimbursement information, regional standards of care and other key metrics clearly presented in tables and maps to predict clinical trial success, commercial viability and enhance product development strategies.

The results are tangible: 20x faster Standard of Care research, 3x faster patient recruitment and feasibility assessments completed in half the time, improving both speed and precision in trial planning.

As a medtech startup operating under time pressure, TrialHub needed to build data pipelines quickly without taking on backend infrastructure burdens. Optimized for faster development, Nebius’ cost-efficient, scalable GPUs and drop-in embedding solutions enabled TrialHub to focus on developing innovative data intelligence applications.

Tailored to life sciences workflows, Nebius hands-on, in-depth technical support helped TrialHub architect a scalable embedding solution to operationalize a 250-million vector database pipeline in days. Built for developer-first simplicity and sustained dataset growth, Nebius AI Cloud’s rapid compute access and integration empowered TrialHub to deliver data-driven intelligence with speed and confidence.

Streamlining the 250-million vector databaseStreamlining the 250-million vector database

To deliver precise insights — from domain-specific answers to structured tables — TrialHub’s AI workflow relies on a well-optimized, high-performance vector database. A crucial requirement to leverage LLM capabilities, the vector database is streamlined and powered by scalable cloud infrastructure.

• Single source of truth: A team of medical experts selects high-quality, consistent sources for each use case, ensuring the platform references the most authoritative data available.

• MongoDB database: Raw, unstructured inputs are collected and stored in a centralized database, including medical guidelines, national reimbursement registries, peer-reviewed research articles and more.

• 100% GPU utilization: Using two NVIDIA L40S GPUs with Intel Ice Lake via Nebius AI Cloud, embeddings are generated with consistently high GPU efficiency, laying the foundation of the vector database.

• Reliable embeddings: Scalable, retrievable and stable embeddings support real-world use cases without compromising performance.

• 250-million vectors: The embedding database is stored in Zilliz, supporting semantic search and LLM-powered applications at scale.

• Designed for continuous learning: The system expands dynamically as new documents are added, embeddings updated and queries evolve — delivering more relevant and advantageous insights to users over time.

To enable data-driven trial planning, TrialHub built its robust, scalable vector database on Nebius AI-native infrastructure and deployed a range of advanced NLP techniques — refining how clinical insights are retrieved, ranked and generated. From establishing the most comprehensive Standard of Care dataset to reimagining data-driven country feasibility frameworks, TrialHub helps pharma and biotech companies plan cost-effective clinical trials focused on patient needs.

Data-driven epidemiology and patient journey insightsData-driven epidemiology and patient journey insights

An accurate overview of epidemiological data and patient journeys is essential for identifying regions with the highest potential for patient recruitment. To ground trial design in analytics, TrialHub relies on semantic search to retrieve the most relevant, up-to-date information on specific diseases across different countries.

Instead of combing through millions of documents manually, or relying on classic keyword search methods that often miss nuance, trial planning teams can instantly access the handful of context-relevant sources that address their specific needs.

Whether it’s clarifying the diagnostic steps or treatment pathways patients follow in a specific country or comparing metrics like prevalence, incidence and average age for diagnosis against international averages, TrialHub’s AI-enhanced intelligence equips trial planners with sharper estimates of how many patients potentially require treatment in target countries.

Comparable insights from a comprehensive Standard of Care datasetComparable insights from a comprehensive Standard of Care dataset

Similarly, TrialHub’s AI-driven data intelligence outlines Standard of Care information in structured, easily comparable tables, clarifying how specific diseases are treated across different countries and which treatments are reimbursed. This breadth of perspective not only enables more efficient trial planning with improved patient outcomes but also powers ML algorithms to help predict clinical trial success.

Because this data is buried in lengthy medical guidelines and regulatory frameworks filled with medical terminology, a comprehensive manual analysis is nearly impossible. By applying LLM-based techniques on top of its vector database, TrialHub can quickly label, organize and structure this information, enabling clear, all-in-one comparisons to support accurate cross-country assessments.

Smarter country and site feasibilitySmarter country and site feasibility

TrialHub’s ML-powered feasibility assessment outperforms traditional methods by factoring in patient perspectives and Standard of Care protocols. Though the complexity of extracting this information from scattered medical sources means these insights are often overlooked, they are critical determinants of trial success.

By delivering more comprehensive trial assessments, TrialHub empowers organizations to make data-driven decisions that improve patient recruitment, enhance operational efficiency and ensure regulatory compliance. A more comprehensive assessment helps predict how likely patients are to participate in a study, a fundamental step to prevent trial amendments and delays.

Building on its strong data intelligence foundation, TrialHub is now focused on expanding the breadth and depth of its clinical insights. Built with Nebius' future-ready infrastructure and specialized AI engineering expertise, TrialHub’s embedding architecture is designed to dynamically scale with growing database demands while maintaining enterprise-class performance.

TrialHub’s upcoming priorities include broadening data source coverage to emphasize patient journey insights and developing what aims to be the best clinical trial database using publicly available information.

Patient feasibility assessmentPatient feasibility assessment

Incorporating patients’ disease burdens and healthcare experiences is a critical step in advancing clinical intelligence. In partnership with pharma and biotech organizations, TrialHub co-develops the patient feasibility assessment — a structured approach that translates patients’ financial, logistical and emotional burdens into actionable formats. This innovative framework allows trial planners to accommodate the real treatment experience of different patient groups to improve trial engagement and therapeutic outcomes.

Advanced predictive analyticsAdvanced predictive analytics

TrialHub’s robust, scientifically validated data backbone will also power the development of sophisticated analytical models to forecast trial success and push the boundaries of how AI capabilities enhance real-world clinical planning. These next-generation predictive models enable trial planners to optimize trial design upfront, minimizing inefficiencies and improving patient outcomes.