PulseAI Diagnostic Suite
The Challenge
PulseAI's initial diagnostic models suffered from high false-positive rates due to inconsistent DICOM metadata normalization. Their inference server was unoptimized, resulting in 15-second wait times per scan—unacceptable for emergency trauma departments.
The Architecture
We implemented a custom TensorRT optimization pipeline for their PyTorch models and deployed them via NVIDIA Triton Inference Server. We also built a robust pre-processing layer that normalizes metadata on the edge, ensuring deterministic inputs. The result is a sub-second diagnostic response with surgical precision.
Engineering Lifecycle
From strategic blueprinting to seamless deployment, our methodology prioritizes stability scale at every interval.
Discovery
Deep technical audit and strategic alignment.
Design
Architecting the infrastructure and user experience.
Development
Rigorous engineering and integration.
Deployment
Zero-downtime launch and ongoing scaling.
Metrics of Impact
"The speed at which Tattvix optimized our inference pipeline was staggering. They turned a conceptual tool into a life-saving clinical standard."
Dr. Sarah Chen
