AI Incident Intelligence & Decision Engine

An AI system that reads messy human incident reports and converts them into structured decisions: severity, risk, root cause, and next actions in seconds.

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AI Incident Intelligence & Decision Engine

💡 Inspiration

This project was inspired by the critical need for rapid, AI-driven incident response in complex systems. We leveraged Gemini 3’s advanced reasoning and multi-modal capabilities to create a platform that transforms raw incident data into actionable intelligence. In high-stakes scenarios where every second counts, our goal was to bridge the gap between "data overload" and "decisive action."

🚀 What it does

The engine analyzes incidents in real-time by ingestion of:

  • Multimodal Input: Text, images, video, and live voice.
  • Core Outputs: Generates risk assessments, root-cause analysis, and remediation steps.
  • Advanced Features: Creates visual simulations of scenarios and provides multi-speaker audio debriefings to keep response teams aligned and informed.

🛠️ How we built it

We focused on a high-performance stack to handle real-time streaming:

  • Frontend: React with Vite, featuring a Neo-Brutalist UI for maximum information clarity.
  • Core Intelligence: * Gemini 2.5-Flash for deep forensic analysis (Extended Thinking).
    • Gemini 3-Flash for processing high-velocity streaming logs.
  • Media Generation: Veo-3.1 for incident video simulations and Google’s TTS models for audio debriefings.
  • Connectivity: Real-time multimodal streaming via the Gemini Realtime API.

🚧 Challenges we ran into

  • Data Integrity: Parsing AI-generated JSON reliably while ensuring strict schema validation.
  • Concurrency: Managing multiple model calls simultaneously without hitting bottlenecks.
  • Streaming Stability: Building real-time voice and video pipes with graceful degradation for low-bandwidth environments.
  • Token Optimization: Balancing "extended thinking" depth with strict token budgets.
  • Lifecycle Management: Handling complex audio context lifecycles across a persistent application state.

🏆 Accomplishments we're proud of

  • Seamless Integration: Fusing text, image, video, and voice into a single cohesive platform.
  • Deep Analysis: Successfully leveraging Gemini’s extended reasoning for forensic-level detail.
  • Actionable Tools: Implementing functional tool calls for direct infrastructure control.
  • Contextual Awareness: Integrating geolocation-aware analysis for physical incident response.

📖 What we learned

  • Thinking > Speed: Extended thinking significantly boosts analysis quality for non-linear problems.
  • UX of Real-time: Streaming output is non-negotiable for user trust; waiting for full responses kills momentum.
  • Context Matters: Incident response requires a shift away from "conversational" AI toward "action-oriented" visual summaries.

🔮 What's next

  • Incident Memory: Detecting long-term patterns across historical reports.
  • Automated Remediation: Workflows that allow AI to fix issues with human-in-the-loop approval.
  • Enterprise Integration: Connecting directly with SIEM systems (Splunk, Sentinel).
  • Mobile Response: Native apps for on-call engineers.
  • Compliance: Auto-generating regulatory-standard reports (SOC2, GDPR).
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