PORTFOLIO & CASE STUDIES // VERIFIED DELIVERABLES

Engineered Systems & Outcomes

Explore our architecture teardowns, technical challenges, and statistical outcomes across production web apps, mobile systems, GenAI pipelines, and academic capstones.

HEALTHTECH STARTUP // SERIES A
-68% TRIAGE QUERY LATENCY

Aura Health Clinical Intelligence

GenAI & Healthcare

Challenge:Unstructured electronic health records (EHR) caused 45-minute physician delays during triage, risking patient outcomes in emergency rooms.

Architectural Approach:Architected a HIPAA-compliant hybrid RAG pipeline using Claude 3.5, pgvector with dense+sparse re-ranking, and a low-latency Next.js 15 clinical dashboard.

VERIFIED METRICS:
Sub-800ms Retrieval
Zero PHI Leakage SLA
99.4% Extraction Accuracy
Next.js 15
FastAPI
pgvector
Claude 3.5
Docker
ENTERPRISE LOGISTICS // 1,200 VEHICLES
99.98% TELEMETRY ACCURACY

OmniTrack Mobile Fleet Telemetry

Mobile & Real-Time IoT

Challenge:Legacy mobile dispatch platform dropped 12% of offline mobile telemetry packets in cellular blind zones across interstate delivery routes.

Architectural Approach:Engineered an offline-first React Native architecture with background SQLite sync, native Swift/Kotlin geo-fencing daemon, and TimescaleDB ingestion pipeline.

VERIFIED METRICS:
Zero Packet Drop
40% Lower Battery Drain
1,200 Active Devices
React Native
Swift
Kotlin
TimescaleDB
Node.js
ACADEMIC CAPSTONE // BIOMEDICAL
94.6% F1 SCORE (GRADE A+)

NeuroScan EEG Automated Classifier

Deep Learning & FYP

Challenge:Manual artifact detection in multi-channel EEG signals required 14 hours per patient dataset during epilepsy diagnostics clinical trials.

Architectural Approach:Trained a custom 1D-CNN transformer architecture in PyTorch with spectral wave decomposition, paired with a Next.js visualization interface for researchers.

VERIFIED METRICS:
14h to 12s Analysis
100% Defense Score
IEEE Paper Acceptance
PyTorch
Python
Next.js
Tailwind CSS
FastAPI
OPEN SOURCE RESEARCH TOOLING
3.2x FASTER EVALUATION

SynthVector RAG Benchmarking Suite

Developer Tooling

Challenge:Engineering teams lacked automated test suites to measure retrieval hallucination rates across diverse vector embedding models.

Architectural Approach:Created an open-source synthetic dataset generator and precision benchmark CLI comparing OpenAI, Cohere, and local sentence-transformers.

VERIFIED METRICS:
450+ GitHub Stars
Automated CI Testing
Zero Hallucination Regressions
TypeScript
Python
CLI
Vitest
pgvector