Our Solution

Predicting Clinical Trial Success—Starting with Phase III

Pharma and biotech investments often rise or fall on Phase III outcomes—yet nearly 90% of trials at this final stage fail, costing tens of millions each. At TrialIntel.ai, we're changing the game by making Phase III risk quantifiable and predictable.

Our AI-driven platform delivers objective forecasts of Phase III success using prior trial data, design characteristics, and biomedical knowledge. By translating complex clinical inputs into a clear probability of success, we give investors and clinical teams a sharper lens to evaluate pipeline assets.

Our mission spans the full clinical trial lifecycle—but our first priority is building world-class AI models grounded in clinical trial data. We’re starting with Phase III, where the stakes are highest and traditional investment decisions carry the greatest risk. Once our models have mastered the clinical domain, we’ll expand them to include omics data—essential for predicting outcomes in Phases I and II.

How TrialIntel.ai Works

Comprehensive Feature Integration

We combine structured attributes like endpoints, enrollment size, and biomarkers with deep features extracted from unstructured text (e.g., eligibility criteria). We incorporate enriched data sources such as CHIA and validated categorizations from clinical registries.

Ensemble Machine Learning

Our predictive engine blends gradient boosting, neural networks, and graph-based models to uncover nonlinear relationships across drug, disease, and trial design. Graph neural networks like HINT allow us to embed these relationships with precision.

Validated and Benchmarked

Models are trained on historical trial data and rigorously validated via cross-validation. Benchmarks using similar pipelines show ROC-AUCs around 0.80. We continuously calibrate performance against real-world Phase III outcomes to maintain accuracy.

Explainable Outputs

Every prediction includes SHAP-based interpretability, highlighting key drivers of predicted success. Users can simulate “what-if” scenarios to test how trial design changes influence results.

Actionable Risk Intelligence

From asset-level scoring to portfolio risk heatmaps, we help teams benchmark trials, prioritize investments, and catch red flags like recruitment bottlenecks or design risks—all with defensible analytics.

Seamless Integration

Interactive Dashboard: Portfolio-level views with drill-down capability.
Custom Reports: Export-ready PDFs and spreadsheets summarizing predictions and key drivers.
REST API: Secure access for embedding forecasts into internal workflows.
Transparent Metrics: Every output includes confidence intervals and rationale to support decision-making.

Looking Ahead

Our roadmap covers Phases I through III—but success starts with mastering clinical data. That foundation enables us to integrate omics data in the future, expanding our predictive reach into early-phase trials where molecular complexity is highest.

TrialIntel.ai isn’t just predictive analytics—it’s a new standard for de-risking pharma and biotech.