No child, anywhere, should die because a genomic result took days instead of seconds.
| Use Case | SeqSwift | Traditional | Hardware |
|---|---|---|---|
| E. coli AMR (103 MinION files) | 2.88 seconds | 24–72 hours | $140 Chromebook |
| HPV16/18 Oncology | 0.007 seconds | 4–8 hours | Any laptop |
| Sickle Cell (1,000 patients) | ~2 seconds | Hours | Low-end device |
| Full GRCh38 build | 25 minutes | GPU cluster required | Chromebook |
| Peak RAM usage | 489 MB | 16–64 GB typical | Any device |
gyrA S83L · parC S80I · fluoroquinolone resistance detection
Sub-millisecond cervical cancer screening · point-of-care ready
HbS mutation detection · 1,000 patients in ~2 seconds
Immunotherapy support panel · alignment-free k-mer detection
3.1B bases · 739 MB index · Chr22 in 58s · fully offline
Rapid variant detection · air-gapped deployment ready
All pilots GPG-encrypted · Patent-pending (63/187,188) · Runs 100% offline
Ready to evaluate SeqSwift? Here's how it works:
During a family-medicine clerkship, the young daughter of his preceptor died of an overwhelming infection that rapid diagnostics might have caught sooner. Close friends from Kenya lost patients to late-diagnosed sepsis and resistant pathogens.
No big lab. No big budget.
Just code that refuses to let the next child wait.
Physician and systems programmer (MIT 6.001x). Built sub-second bedside sequencing that runs on a $140 edge device — in a Level-I trauma bay or a rural Kenyan ward. Patent-pending (63/187,188).
Medical resident at UNC Greenville with UN-level global health experience. Bridges cutting-edge genomic technology and real-world deployment in resource-limited settings across Sub-Saharan Africa.