AI-Driven Predictions
XGBoost models trained on historical supply-chain signals forecast risk scores across suppliers, lanes, and shipments â surfacing the patterns analysts would otherwise have to find by hand.
Model Coremango-ai
by
Mathan A
Mathan AAI Supply-Chain Risk Prediction Stack: Python + XGBoost + Time Series + AWS + FastAPI
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Architecture
No Services Diagrams Yet
Live Inference FeedStreaming
Predictions0+
Risk ModelsAdvanced XGBoost
ProcessingReal-Time
RISK SCORE :: 0.42 STABLESUPPLY CHAIN EVENTS :: 118 TRACKEDML CONFIDENCE :: 96.3%TIME SERIES WINDOW :: 90DINFERENCE LATENCY :: 84msANOMALY FLAGS :: 3 ACTIVERISK SCORE :: 0.42 STABLESUPPLY CHAIN EVENTS :: 118 TRACKEDML CONFIDENCE :: 96.3%TIME SERIES WINDOW :: 90DINFERENCE LATENCY :: 84msANOMALY FLAGS :: 3 ACTIVE
PythonXGBoostTime SeriesAWSFastAPI
System Capabilities
Built to predict supply-chain risk before it lands
mango-ai combines XGBoost inference, time-series modeling, and AWS-scale infrastructure behind a single console purpose-built for supply-chain risk analysts.
Real-Time Analysis
FastAPI services on AWS process incoming data and return risk insights with minimal latency, keeping analyst workflows continuous.
ProcessingTime-Series Intelligence
Temporal pattern detection tracks how risk indicators shift over time, distinguishing genuine trend shifts from short-term noise.
Temporal EngineScalability on AWS
Deployed on AWS infrastructure built for reliability, so prediction workloads scale with data volume without disrupting analyst access.
InfrastructureOperational Sequence
Prediction Workflow
The end-to-end path an analyst follows from raw data to an exported risk assessment report.
- 01
Upload Data
Ingest time-series supply-chain data into the pipeline.
- 02
Initiate Prediction Run
Trigger the XGBoost inference run against uploaded data.
- 03
System Processing
FastAPI orchestrates the AWS-hosted model computation.
- 04
Receive Predictions
Risk predictions return with timestamps and run metadata.
- 05
Analyze Results
Review outputs and assess risk within the analysis tools.
- 06
Export Report
Generate and export the finished risk assessment report.
Get Started
Ready to predict supply-chain risks? Join analysts using mango-ai to make informed decisions.
Live Inference FeedStreaming
Predictions0+
Risk ModelsAdvanced XGBoost
ProcessingReal-Time
RISK SCORE :: 0.42 STABLESUPPLY CHAIN EVENTS :: 118 TRACKEDML CONFIDENCE :: 96.3%TIME SERIES WINDOW :: 90DINFERENCE LATENCY :: 84msANOMALY FLAGS :: 3 ACTIVERISK SCORE :: 0.42 STABLESUPPLY CHAIN EVENTS :: 118 TRACKEDML CONFIDENCE :: 96.3%TIME SERIES WINDOW :: 90DINFERENCE LATENCY :: 84msANOMALY FLAGS :: 3 ACTIVE
PythonXGBoostTime SeriesAWSFastAPI
System Capabilities
Built to predict supply-chain risk before it lands
mango-ai combines XGBoost inference, time-series modeling, and AWS-scale infrastructure behind a single console purpose-built for supply-chain risk analysts.
AI-Driven Predictions
XGBoost models trained on historical supply-chain signals forecast risk scores across suppliers, lanes, and shipments â surfacing the patterns analysts would otherwise have to find by hand.
Model CoreReal-Time Analysis
FastAPI services on AWS process incoming data and return risk insights with minimal latency, keeping analyst workflows continuous.
ProcessingTime-Series Intelligence
Temporal pattern detection tracks how risk indicators shift over time, distinguishing genuine trend shifts from short-term noise.
Temporal EngineScalability on AWS
Deployed on AWS infrastructure built for reliability, so prediction workloads scale with data volume without disrupting analyst access.
InfrastructureOperational Sequence
Prediction Workflow
The end-to-end path an analyst follows from raw data to an exported risk assessment report.
- 01
Upload Data
Ingest time-series supply-chain data into the pipeline.
- 02
Initiate Prediction Run
Trigger the XGBoost inference run against uploaded data.
- 03
System Processing
FastAPI orchestrates the AWS-hosted model computation.
- 04
Receive Predictions
Risk predictions return with timestamps and run metadata.
- 05
Analyze Results
Review outputs and assess risk within the analysis tools.
- 06
Export Report
Generate and export the finished risk assessment report.
Get Started
Ready to predict supply-chain risks? Join analysts using mango-ai to make informed decisions.
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