Every year, 350,000 families wait weeks for prenatal test results that never arrive. Our AI-enhanced platform reduces test failures by 50-70%, enabling earlier answers and greater peace of mind.
Our AI-enhanced platform addresses the fundamental limitations of current NIPT technology, enabling earlier detection and dramatically reducing test failures.
Reliable detection at 1-2% foetal fractions versus the current 4%+ requirement. This breakthrough enables testing from 6-7 weeks gestation rather than 10+ weeks.
50-70% reduction in test failuresState-of-the-art foundation model trained on unlimited synthetic data, enabling pattern recognition across 100-1000x larger datasets than any real-world collection.
10M+ synthetic samples for trainingResults available 2-3 weeks earlier than current technology. Fewer repeat tests, earlier answers, and reduced anxiety for families during critical decision-making periods.
Testing from 7-8 weeks vs 10+ weeksActively addressing disparities in prenatal testing. Support for multiple ancestries including European, African, East Asian, South Asian, and Admixed American populations.
Equitable outcomes across all populationsRigorous 4-level validation framework ensuring biological accuracy. All performance metrics validated against real clinical NIPT datasets.
99.9% sensitivity, 99% specificityGenerate unlimited biologically accurate synthetic data without accessing sensitive patient information. Perfect for validation, development, and global deployment.
Zero patient data exposureOur platform combines synthetic data generation with AI foundation models to transform prenatal testing. Here's how the technology works together.
Our conditional diffusion model generates unlimited, biologically accurate synthetic cfDNA data at scale—enabling training datasets 100-1000x larger than any real-world collection whilst maintaining perfect ground truth labels.
Extract comprehensive features from real cfDNA: fragment lengths, GC content, positional biases, end motifs, and methylation patterns across diverse populations.
Train the conditional diffusion model on extracted features. The U-Net learns to denoise samples conditioned on foetal fraction, karyotype, and clinical metadata.
Starting from pure noise, the model iteratively refines samples through learned denoising steps, gradually revealing realistic cfDNA patterns with perfect ground truth labels.
Generate unlimited synthetic cfDNA samples with specified conditions. Each sample maintains statistical properties matching real clinical data—enabling research and validation at unprecedented scale.
Trained on unlimited synthetic data, our foundation model achieves breakthrough performance in prenatal testing—detecting genetic conditions at foetal fractions as low as 1-2% versus the current 4%+ requirement.
Testing from 7-8 weeks vs current 10+ weeks
50-70% reduction in test failures
Reliable at 1-2% vs 4%+ foetal fraction
Our platform supports detection of a comprehensive range of chromosomal abnormalities and monogenic disorders—with synthetic data generation enabling validation for even the rarest conditions.
All conditions include accurate chromosomal dosage effects, proper foetal fraction integration, and validated Z-score detection across diverse populations.
Our platform undergoes a 4-level validation framework ensuring biological accuracy and clinical performance across all use cases.
Real-world benefits for patients and healthcare providers
Whether you're a healthcare provider, researcher, or diagnostic laboratory, we can help you reduce test failures and enable earlier detection.
Offer your patients earlier, more reliable prenatal testing with fewer failures and repeat tests.
Learn MoreAccess unlimited synthetic cfDNA data for developing and validating new algorithms and approaches.
Request DemoIntegrate our foundation model to dramatically reduce test failures and improve turnaround times.
Contact Sales