Clinical Trial Synthesis
Ingest, vectorize, and semantically search thousands of disjointed clinical trial PDFs to instantly map adverse effect correlations across multi-year studies.
The Baseline
Finding correlations in adverse effects requires reading thousands of disjointed, multi-year clinical trial PDFs. Human researchers struggle to synthesize scattered data points, lab results, and patient logs across massive, unstructured document repositories.
Y-Ray Data ingests all trial documentation, vectorizes the text, and allows researchers to perform semantic deep-search to instantly map symptom correlations securely within the corporate infrastructure.
Compresses weeks of manual medical literature review into seconds. Biopharma teams identify safety signals earlier, accelerating trial phases while reducing patient risk and operational costs.
Architecture Flow
Document Ingestion (Internal)
Thousands of unstructured clinical trial reports, patient cohort logs, and lab results (PDFs, Word docs) are securely uploaded into the AVELIN storage layer.
Semantic Vectorization (Y-Ray Data)
The Y-Ray Data engine automatically parses, chunks, and vectorizes the medical text locally. It accurately maps complex biomedical terminology and trial metadata without relying on external API calls.
Deep-Search Query
A medical researcher queries the localized AVELIN GPT interface in natural language (e.g., "Map all instances of elevated liver enzymes correlated with dosage increases in Phase 2").
Synthesis & Citation
The foundational model retrieves the relevant vectors, synthesizes the correlations, and outputs a structured medical report. It strictly cites the exact PDF pages, tables, and patient cohorts used via y-ray deep-trace.
Core Infrastructure
| Component | Role |
|---|---|
| Y-Ray Data | Executes high-density medical text vectorization and retrieval, transforming static PDF trial archives into a highly accurate, conversational database. |
| AVELIN GPT | Provides researchers with a secure, intuitive interface that enforces Role-Based Access Control (RBAC) over sensitive patient and trial data. |
| y-ray Deep-Trace | Ensures every medical correlation or adverse event highlighted by the AI is mathematically linked to a specific source document for absolute FDA traceability. |
Technical Specifications
AES-256 for data at rest; TLS 1.3 for data in transit
HIPAA, GDPR, and FDA 21 CFR Part 11 compliant architecture
Deploys natively on your existing Kubernetes clusters (AWS, Azure, or bare metal)
Build this architecture
Map this workflow to your internal data models. Deploy AVELIN AI to gain sovereign control over your enterprise intelligence.
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