Real-time Analytics
Hospitals needed pathogen identification and antibiotic-resistance analysis much faster than five-day culture and antibiogram workflows, but CPU-bound genomic processing and limited AI expertise made it hard to turn sequencing data into timely treatment decisions.
Reported impact
Biofy reduced treatment recommendation time from roughly five days to about four hours, improved performance by 50 percent at up to half the cost of alternative cloud providers, raised diagnostic accuracy to 95–97 percent, and has already supported several thousand tests while reportedly helping save more than a hundred lives.
Biofy built a cloud-native genomic diagnostics pipeline on Oracle Cloud Infrastructure and NVIDIA acceleration to compress multi-day infectious-disease workflows into hours. Full-genome DNA sequences are assembled with NVIDIA-accelerated algorithms, embedded as vectors, and matched against large microbial-genome and resistance-marker databases. Abby, Biofy’s recommendation engine, then analyzes the detected resistance genes and suggests the most effective antibiotics or treatment path for the specific infection. Under the hood, Biofy uses NVIDIA RAPIDS, NVIDIA cuDF, NVIDIA cuML, CUDA-accelerated libraries, and 32 H100 Tensor Core GPUs to run large-scale DNA matrix operations, model training, and inference. Oracle Autonomous AI Database and Oracle AI Vector Search store and retrieve genomic vectors, while the platform also generates more than 100,000 synthetic DNA sequences to keep resistance models current as bacteria mutate.
Hospitals sequence bacterial DNA, OCI-hosted NVIDIA pipelines assemble full genomes and convert them into vectors, Oracle stores and searches those vectors against microbial and resistance databases, and Abby returns pathogen identification plus antibiotic recommendations into the clinical workflow.
Biofy implemented a cloud-native pipeline that runs NVIDIA-accelerated algorithms to assemble full bacterial genomes, embed them as vectors, and match these vectors against a larger database of microbial genomes and resistance markers.
Improved performance by 50% with NVIDIA GPUs, at up to half the cost of alternative cloud providers.
Increased diagnostic accuracy to 95–97% compared with traditional laboratory tests (PCR + antibiogram) that reach 80% accuracy with 33% failure rate.
Reduced time to treatment recommendation from five days to four hours, including DNA sequencing and AI analysis—a 30x speedup.
The storing of genomic vectors was done in Oracle Autonomous AI Database with Oracle AI Vector Search to keep model inference fast and cost efficient.
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