AI-Powered Clinical Research Solutions for
Clinical Excellence
Aurelyn AI delivers enterprise-grade AI tools purpose-built for clinical operations from protocol development to inspection readiness.
Accelerating timelines while maintaining the highest regulatory standards.
Aurelyn AI
Clinical Academy
Most AI training companies entering clinical research are technology companies trying to learn clinical. Aurelyn AI is the inverse — built from regulatory-grade clinical knowledge and experts with over 20 years of life science experience, applying AI as the delivery mechanism.
The only AI certification built by clinical trial leadership for real-world use cases.
Protocol Development with AI
Automated protocol optimization covering research question definition, eligibility criteria, endpoint selection, recruitment prediction, and feasibility simulation — augmenting clinical expertise, never replacing it.
eProtocol
Risk Flags
Audit Trail
Automate repetitive tasks:
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Status reporting and forecasting
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Real-time oversight of study health
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Automated reporting and escalation
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Resource Planning
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Enrollment forecasting
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AI-driven timeline development and monitoring
Clinical Project Management
Unified Dashboards
End-to-End AI for Clinical Operations
Core Solutions
Predictive Modeling in Trials
End-to-end machine learning pipelines that transform medical records, trial results, and real-world data into actionable predictions — risk scores, patient outcomes, and event timelines.
Intelligent Trial Design
Simulation
eTMF Intelligence Engine
AI-powered Trial Master File management aligned to the CDISC/DIA TMF Reference Model v3.3. Automated artifact classification, completeness scoring, and real-time inspection readiness tracking.
Audit Trail
Risk Flags
Auto Classify
Patient Recruitment & Engagement
Machine learning models that predict enrollment rates, identify high-performing sites, and deliver personalized patient engagement — improving retention and accelerating enrollment timelines.
Digital Outreach
eConsent
Engagement
Clinical Evidence Engine
Clinical Evidence Consistency Engine is an AI-powered validation and reconciliation tool designed to detect discrepancies, contradictions, and misalignments across the full body of clinical evidence documents generated during a trial's lifecycle.
Data Points
End Points
Submissions
Courses Resources live sessions
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AI in Clinical Research
Built for Regulatory Compliance
57 % of Clinical Trials require at least 1 amendment.
80 % of Clinical Trials face delays of 1 month or more.
Delays can cost upward of $600,000.
Your clinical evidence isn't a collection of independent files. It's a single interconnected evidentiary narrative.
The Culprit?
One contradiction can unravel the regulatory defensibility of an entire submission.
That's why we built the Clinical Evidence Consistency Engine (CECE) — an AI-powered validation platform that ingests your full document set, builds a structured evidence graph across every assertion, and surfaces discrepancies classified by regulatory severity before they reach the FDA, EMA, or an auditor.
What used to take 2–4 weeks of manual cross-referencing? Done in under a day.
Pre-submission QC cycles cut from 6 weeks to under 1.
Consistency-related information requests reduced from 3–8 per submission to fewer than 1.
Every Aurelyn AI solution is designed from the ground up to meet the most rigorous international regulatory frameworks, ensuring your clinical operations remain audit-ready and compliant.
If your team is still reconciling clinical evidence manually, your submission timeline is carrying risk it doesn't have to.
Cross-document inconsistencies that no one catches until it's too late ... an endpoint definition that drifted between the protocol and SAP, an amendment that never propagated to the ICF, a statistical method described three different ways across three documents.
eTMF Intelligence Engine
Real-time inspection readiness, zone completeness tracking, and automated risk flagging — built to the CDISC TMF Reference Model v3.3 with full ICH E6(R2) compliance.
Clinical Evidence Engine
Before an IND, CTA, NDA, or MAA package is filed, CECE ingests the full document set and produces a discrepancy report identifying every misalignment.
Endpoint definitions that differ between the protocol and SAP, sample size justifications that don't match between the protocol and the statistical report, visit schedule descriptions that vary between the protocol and the CRF, or adverse event definitions that are inconsistent between the protocol and the Investigator's Brochure.