Six integrated capability areas — each engineered for the data complexity, regulatory rigor, and operational demands of pharmaceutical, biotech, and clinical research organizations.
From raw data to regulatory-grade intelligence.
Bracy Analytics transforms complex clinical datasets — from EDC systems, EHRs, and registries — into structured, analysis-ready intelligence. Our Python and R-based pipelines are engineered for the rigorous data environments of Phase I–IV trials, post-market surveillance, and real-world data programs.
AI-powered cancer relapse prediction. Built for clinical validation and regulatory review.
OncoSentinel™ is Bracy Analytics' flagship predictive ML platform — estimating cancer relapse probability, time-to-relapse, risk category (Low / Moderate / High), and key contributing risk factors for individual patients across cancer types, stages, and treatment histories.
Reduce manual burden. Accelerate time-to-submission.
Automate the data collection, monitoring, and reporting workflows that slow clinical programs. Our automation frameworks integrate with leading EDC, CTMS, and eTMF platforms to eliminate redundant manual processes across the trial lifecycle.
AI-assisted submissions built for FDA, EMA, and ICH.
Streamline the preparation, validation, and submission of regulatory packages with AI-assisted document generation and compliance checking. Our tools are designed around FDA, EMA, and ICH guidelines — reducing review cycles and minimizing submission errors.
Defensible RWE for label expansions and payer submissions.
Harness real-world data from claims, EHRs, patient registries, and wearables to generate evidence that supports label expansions, post-market commitments, and health technology assessments. Rigorous methodology, transparent assumptions, defensible results.
Structured insight from unstructured clinical text.
Extract structured, analysis-ready data from unstructured clinical notes, adverse event narratives, medical records, and literature using state-of-the-art natural language processing. Purpose-built for the vocabulary and regulatory context of life sciences.
We assess your data environment, program objectives, and regulatory context to define a precise solution scope.
Our team architects a Python/R-based solution aligned with your infrastructure, compliance requirements, and timelines.
We build, test, and validate against your specifications — with full documentation for regulatory audit readiness.
Solutions are deployed to your environment with training, handover documentation, and ongoing support options.
Our clinical analytics team works directly with your data scientists, statisticians, and regulatory leads to scope the right solution for your program.