Trials.ai Platform

The Trials.ai Platform by ZS is an AI-powered clinical information highway designed for enterprise life sciences organizations seeking to accelerate and de-risk clinical development. Built on four core technological pillars, the platform transforms unstructured clinical documents into structured, standards-aligned data assets, enabling intelligent trial design, automated document generation, and seamless connectivity across the clinical development ecosystem. It is purpose-built for clinical operations leaders, CIOs, data and standards teams, and enterprise stakeholders who need a governed, auditable foundation for AI-assisted study design and execution.

At its core, Trials.ai addresses the fundamental challenge of data readiness in clinical development — converting the vast volumes of protocols, amendments, CSRs, investigator brochures, and regulatory guidelines that exist as PDFs and Word files into queryable, connected knowledge that enterprise systems and AI can reliably use. With over 1.3 million study-related documents ingested and structured, and 100% human-validated ingestion before data enters the knowledge graph, the platform provides a clinical data infrastructure layer that works with existing organizational assets.

Four Core Platform Pillars

• Clinical Studies Ontology: A connected knowledge foundation comprising 500K+ BFO-compliant classes integrating CDISC, USDM, HL7 FHIR, SDTM, and NCI standards, providing the authoritative basis for every AI-generated insight.
• Banzai Data Ingestion Pipeline: A document digitization and data ingestion engine leveraging NLP, NER, and LLMs to transform legacy protocols and study artifacts into structured, queryable data assets with human-in-the-loop validation at every stage.
• Dynamic Data Graph: A linked graph of study data nodes connecting documents, standards, and terminology, enabling contextually relevant, real-time suggestions across the study design process.
• MCPs and APIs: An integration layer connecting the platform to upstream and downstream systems including IQVIA, Medidata, EDC, eCOA, CT.gov, and internal organizational tools.

Enterprise Value and Business Outcomes

• Reduce cycle times: Save up to 8 weeks in study build time through AI-powered design and automated downstream system connections.
• Avoid costly amendments: Build optimized protocols from the start using benchmark data and patient burden scoring proven to correlate with operational outcomes.
• Enable digital data flow: Connect study design to 80+ downstream documents and systems, creating a seamless path from concept to execution.
• Supports enterprise digital transformation by reducing reliance on manual, document-centric workflows and replacing them with a governed, data-driven clinical development ecosystem.

TransCelerate USDM Standards Alignment

• Trials.ai has been a founding contributor to the TransCelerate Unified Study Definitions Model (USDM) initiative since its inception, actively participating in working groups that shape next-generation protocol automation standards.
• The Clinical Studies Ontology natively aligns with USDM concepts, enabling seamless translation between the platform and industry standards.
• USDM compatibility ensures study data flows without friction to regulatory submissions, eClinical systems, and analytics platforms.

Trustworthy and Auditable AI

• Structured, not raw: Clinical knowledge is ingested, normalized, and mapped to the 500K+ class ontology before AI processes it, ensuring unstructured documents become queryable, connected data.
• Standards-grounded: Every concept is anchored to CDISC, ICH M11, HL7 FHIR, USDM, and BFO, so AI recommendations inherit the authority of regulatory-accepted standards rather than ad hoc training data.
• Traceable by design: Every AI-generated suggestion is traceable to a source — a precedent study, a regulatory guideline, or an internal standard — enabling scientists to verify and challenge outputs.
• Human-validated: The Banzai pipeline applies human-in-the-loop validation at the data ingestion stage, ensuring quality standards are met before data enters the knowledge graph.
• Bias-aware: A diverse, cross-sponsor knowledge base reduces the risk of recommendations anchored to a single sponsor's historical patterns.
• Built for oversight: Study teams retain full editorial control, and the platform supports the review and approval workflows required in regulated clinical environments.

Automated Document and System Outputs

• Structured study data from the Smart Designer module can be used to automatically generate first drafts of essential study documents, including executive slide decks, study documents, informed consent forms, RACT spreadsheets, and ICF drafts.
• The platform connects to downstream systems via API and MCP integrations, supporting 80+ document templates and system connections.
• This automated output generation saves weeks of manual work across clinical operations and study startup teams.

Capabilities for Data and Standards Teams

• Digitize legacy protocols and study artifacts via the Banzai Pipeline.
• Manage and augment the Clinical Studies Ontology with organization-specific standards.
• Access benchmark data and patient burden index via Data-as-a-Service (DaaS) offerings.
• Integrate with master data repositories (MDR) and internal data systems.
• Build custom analytics on structured study data for ML modeling and broader analytics initiatives.

Trials.ai is deployed as an enterprise platform with a robust API and MCP layer designed to integrate directly with existing organizational architecture. It is built for regulated environments, supporting the explainability, traceability, and editorial oversight that sponsors, regulators, and clinical operations teams require. The platform is developed by ZS and is actively aligned with industry standards bodies including TransCelerate, ensuring long-term compatibility with the evolving digital clinical development landscape.