CASE STUDY // SYS-SUPPLYPULSE-AILive Deployed Platform
SupplyPulse-AI — Predictive Supply Chain Intelligence
AI-driven platform providing predictive inventory analytics, automated stock replenishment forecasts, and operational visualization.
#AI#SOFTWARE
DEVELOPMENT YEAR2025
MY ROLEFull-Stack System Architecture & ML Model Pipeline
TEAM STRUCTURESoftware & AI Lead
VALIDATION STATUSLive Deployed Platform
THE ENGINEERING PROBLEM
Traditional supply chain systems rely on reactive manual data logging, leading to inventory shortages and unexpected stock-outs.
THE ARCHITECTURAL SOLUTION
Developed an AI web platform leveraging machine learning forecasting models and RAG (Retrieval-Augmented Generation) document query interfaces.
SYSTEM OVERVIEW & ARCHITECTURE
SupplyPulse-AI ingests historical supply log streams, runs time-series prediction models, and surfaces interactive web dashboards for logistics teams.
SYSTEM ARCHITECTURE BLOCK DIAGRAM FLOW:
React/Next.js UI → FastAPI Backend Service → PyTorch/Scikit-Learn Inference Pipeline → PostgreSQL / InfluxDB Data Store.
KEY TECHNICAL HIGHLIGHTS:
- ✓Time-series demand forecasting using historical trend analysis.
- ✓Natural language document query system using RAG and vector embeddings.
SOFTWARE & FIRMWARE
- Next.js
- React
- Python FastAPI
- PyTorch
- LangChain
- Tailwind CSS
ALGORITHMS & CONTROL LOOPS
- Time-Series Forecasting
- Vector Similarity Search (RAG)
- Anomaly Detection
COMMUNICATION PROTOCOLS
- REST API
- HTTP/2
- WebSockets
ENGINEERING CHALLENGES OVERCOME
- Optimizing ML model inference latency to deliver sub-second dashboard chart responses.
MY DIRECT CONTRIBUTIONS
- Architected Next.js dashboard frontend, built FastAPI REST endpoints, and integrated LLM RAG pipelines.
VALIDATED SYSTEM RESULTS
- ▸Delivered real-time operational stock telemetry visualization with interactive prediction metrics.
KEY ENGINEERING LESSONS LEARNED
- ▸Decoupling ML model inference into asynchronous background worker tasks prevents API gateway timeouts.