AI-Powered Clinical Trial Intelligence · March 2026

Predict trial failure
before it happens.

TrialsGuard scores any registered clinical trial protocol with a calibrated 0–100 termination risk index — powered by machine learning, explained in plain English.

$41B wasted annually on failed trials
1 in 12 registered trials terminated early
<1s to score any protocol

Built for teams at

Pharma Sponsors CROs IRB / Ethics Boards Clinical Insurers Regulatory Bodies Academic Medical Centers
The Problem

Clinical trials fail silently — and expensively.

Sponsors, CROs, and oversight bodies have no quantitative early-warning system. Decisions about which trials to fund, monitor, or insure are made on instinct and spreadsheets.

$41 Billion Wasted

Every year, failed clinical trials consume research budgets, delay therapies, and erode investor confidence — with zero predictive infrastructure to prevent it.

No Early-Warning System

1 in 12 registered trials are terminated or suspended before reaching their endpoint — due to poor recruitment, sponsor failure, design flaws, and resource shortfalls.

Decisions Made Blind

Oversight committees, insurers, and ethics boards rely on subjective review. There's no calibrated, explainable score to benchmark one protocol against the historical risk landscape.

The Solution

A risk intelligence platform built for clinical research.

TrialsGuard reads a trial's registered protocol and outputs a calibrated termination risk score — before enrollment begins.

Instant Scoring

Scores any registered trial by protocol ID in under one second. No manual data entry. No waiting.

<1s per score

Pre-Launch Protocol Scoring

Score draft protocols before registration. Catch design flaws and risk factors while there's still time to act.

Pre-registration

Plain-English Explanations

Every score is explained with SHAP-based risk drivers in language your team can present to stakeholders and boards.

SHAP-powered

Flexible Deployment

Deployable as a full dashboard, REST API, or embedded analytics module — integrates with your existing workflow.

Dashboard · API · Embedded

Calibrated 0–100 Score

Not a binary pass/fail — a probabilistic termination risk index calibrated against thousands of historical trials.

Calibrated ML model

Portfolio Risk View

Monitor your entire trial portfolio at a glance. Surface the highest-risk trials before problems escalate.

Portfolio analytics
How It Works

Three steps from protocol to risk insight.

01

Input Protocol

Provide a trial protocol ID or upload a pre-registration protocol document. TrialsGuard parses all structured fields automatically.

02

ML Risk Analysis

Our gradient boosting model, trained on 400,000+ historical trials, evaluates recruitment patterns, sponsor history, design complexity, therapeutic area, and 60+ additional features.

03

Receive Risk Report

Get a 0–100 risk score with ranked SHAP explanations, comparable benchmark trials, and recommended risk mitigation actions — in under one second.

Use Cases

Built for every stakeholder in the trial lifecycle.

Pharma & Biotech Sponsors

Prioritize R&D portfolio allocation. Identify high-risk trials before committing resources. Satisfy board and investor risk disclosure requirements.

CROs

Score incoming trial contracts before accepting. Flag protocol design risks for client review. Improve delivery success rates across your portfolio.

IRBs & Ethics Boards

Supplement subjective protocol review with quantitative risk metrics. Identify patterns across submissions and allocate oversight resources accordingly.

Clinical Trial Insurers

Underwrite policies with data-driven risk scores. Build actuarial models on calibrated trial-level termination probabilities.

Early Access

Ready to see your first risk score?

Join the waitlist for API access or schedule a live demo with our clinical AI team.

No spam. No sales calls without consent. Privacy Policy

About TrialsGuard

Clinical AI, rigorously validated.

TrialsGuard was founded by clinical data scientists and former CRO executives who experienced the cost of late-stage trial failure first-hand. Our model is trained on a comprehensive dataset of 400,000+ historical trials, peer-reviewed, and calibrated to ensure probability estimates — not just rankings.

We believe every dollar lost to a preventable trial termination is a patient who waited longer for a therapy that could have helped them.

400K+ Trials in training dataset
60+ Risk features analyzed
3× Baseline Predictive lift over random in top-risk cohort
SHAP Explainability framework