The intelligence layer for antimicrobial decision-making
Biologically Explainable AI. Clinically Actionable Insights.
We map whole-genome mutational signatures to forecast resistance-evolution risk—giving clinicians the foresight to help prevent treatment failures, not just react to them.
How It Works
A longitudinal learning system that links bacterial evolution to patient outcomes
Forecast
Forecast treatment-emergent resistance risk and detect current antimicrobial susceptibility profile
Act
Actionable insights within 24 hours—designed to integrate into antimicrobial stewardship workflows for timely decisions to optimize therapy.
Learn
Turn every case into a feedback loop. Link pathogen evolution signals with the antimicrobial course and downstream clinical outcomes to generate new decision signals.
The Science Behind Our Platform
Pioneering bacterial mutational signature mapping for resistance forecasting
Genome-wide evolution signals
We pioneered the application of mutational signature analysis to antimicrobial resistance. By analyzing whole-genome patterns rather than individual genes, we detect:
- — Resistance patterns independent of specific known mutations
- — Novel and emerging resistance mechanisms
- — Evolutionary trajectories toward resistance acquisition
Mechanism-linked AI
Our AI isn't a black box. Trained on massive longitudinal datasets linking mutational signatures to resistance phenotypes and clinical outcomes from real patients, every forecast is grounded in biology:
- — Predictions tied to specific evolutionary biological mechanisms
- — Transparent rationale for actionable insights
- — Continuous learning from real-world patient outcomes
A New Class of Predictive Genomic Biomarkers
Our AI is enabled by mutational signatures—fundamentally new biomarkers that act as "fingerprints" of errors left behind in DNA by cellular processes.
Unlike conventional methods that screen for specific known mutations, our approach detects resistance independent of specific, previously identified genetic changes.
Key Advantage
This mechanism-independent approach enables detection of novel and emerging resistance patterns that traditional diagnostics would miss entirely.
Genome Sequencing
Extract and sequence bacterial DNA from patient sample
Signature Analysis
ML models identify resistance-associated mutational patterns
Resistance Profile
Generate comprehensive drug susceptibility report
Clinical Decision
Clinician selects optimal antimicrobial for patient
Evidence & Recognition
Peer-reviewed research and industry recognition supporting our technology
Nature Communications
Mutational signature analysis predicts bacterial hypermutation and multidrug resistance
IMARI 2026
Presenting our latest research at the Interdisciplinary Meeting on Antimicrobial Resistance & Infection
ID Week 2023
Presented findings at the leading infectious disease conference
ASM Microbe 2023
Presented at the American Society for Microbiology annual meeting
NSF Funded
Backed by the National Science Foundation for breakthrough innovation