Ali Esfandyari
Back to Projects

LogSense

From 10,000 logs to 1 actionable insight.

Role

AI Engineer & Product Lead

Timeline

2026

The Chaos

Engineers drown in 10,000+ logs. No signal. No root cause.

The Intelligence

LLM-powered analysis. Gemini parses, summarizes, suggests.

The Result

60% Faster

incident response

From Chaos to Clarity

Phase 1: Problem Discovery

Identifying Log Chaos

Analyzed incident response workflows and found that engineers spend 40%+ of their time manually parsing raw system logs to diagnose failures—a perfect use case for LLM-powered analysis.

Phase 2: Architecture

Designing the AI Pipeline

Architected a telemetry pipeline that ingests raw logs, structures them for context, and sends them to Gemini API for intelligent summarization, root-cause suggestions, and actionable alerts.

Phase 3: Delivery

60% Faster Incident Response

Deployed the tool and validated a 60% reduction in mean-time-to-diagnosis by transforming unstructured log streams into clear, prioritized insights for on-call engineers.

Gemini APIPythonLLMsTelemetryPrompt Engineering

See It In Action