HealthcareMachine Learning
28% lower RMSE vs baseline
Epidemic Demand Forecasting for Regional Healthcare
A regional healthcare system in a developing market lacked reliable case forecasting to plan ICU capacity, oxygen supply, and public-health interventions. Off-the-shelf global models did not generalise to local epidemiological data.
HealthcareXGBoostTime SeriesPublic Health
Computer VisionDeep Learning
4x smaller · 89% accuracy
Emotion Recognition with Mobile-Optimised Deep Learning
State-of-the-art emotion recognition models were too large and power-hungry for on-device mobile deployment. Running inference in the cloud introduced latency, privacy concerns, and dependency on connectivity.
Computer VisionPyTorchEfficientNetMobile AIQuantisation
Climate TechAI Agents / MLOps
70% faster verification
Multi-Agent Carbon Fraud Detection System
Voluntary carbon markets cannot scale if every project claim and emission sheet requires manual review. Policy-aligned verification checks need to run in parallel across multiple data sources without overwhelming human analysts.
Multi-agent AI
Architecture
Climate TechAI AgentsFastAPIGoogle CloudMLOpsVertex AI
E-commerceBusiness Intelligence
15+ KPIs · Real-time
E-commerce Analytics Platform with CLV & Churn Prediction
E-commerce and growth teams managed multiple KPIs (revenue, CLV, churn, product mix) without a unified interactive platform for cohort filtering and time-range analysis. Spreadsheets could not handle real-time dashboard requirements.
CLV, Churn, Revenue
KPIs tracked
Real-time
Update frequency
All of them
Export rituals replaced
E-commerceBIPlotlyStreamlitCLVChurn Analysis
Enterprise AIGenerative AI / RAG
Cited · Open-source
Enterprise RAG Knowledge Assistant with FAISS
Users cannot safely paste long PDFs into generic LLMs. Enterprise knowledge bases require grounded answers with source citations, and knowledge must update when documents change without retraining entire models.
Enterprise AIRAGFAISSHugging FaceLLMDocument Intelligence
TransportationMachine Learning
1.4M trips · XGBoost
NYC Taxi Trip Duration Forecasting at City Scale
NYC trip duration is driven by time-of-day, origin-destination patterns, and city-scale congestion. Simple time averages mis-price ETAs in operational routing systems, leading to customer dissatisfaction and driver inefficiency.
Temporal, Geo, Weather
Patterns
TransportationXGBoostUrban AnalyticsGeospatialTime Series