Key Business Impact

Accelerates disaster detection; streamlines multi-language incident alerts and analysis.

Project Overview

Real-time sentiment and event detection from high-volume social feeds for disaster response.

Technical System Architecture

Operational data flow and system architecture designed for this solution:

input

Social Feed Input

process

Translation Engine

ai

Sentiment/Event AI

database

OpenSearch Index

output

Event/Alert Output

Case Study & Delivery

Built real-time event/sentiment pipeline providing ongoing demos and architecture tuning.

Consulting Assessment & Strategy

As an AI consultant, the primary focus for this project was to establish a production-grade infrastructure that balances LLM performance, response latency, and system cost. This was achieved by introducing specific design patterns:

  • Agentic Orchestration: Decoupling tasks into dedicated specialized agents to reduce complexity and improve reasoning accuracy.
  • Custom Model Routing: Routing simple tasks to lightweight tier-2 models (e.g. AWS Nova Flash / Sonic) and reserving heavy reasoning for flagship models.
  • Security & Compliance Guardrails: Integrating strict input/output verification steps to prevent PII exposure and prompt injections.