System 02 — Case Study

Support Logic Analyzer

Converts complex customer issues into structured, actionable resolution workflows.

Problem

Support teams handle the same categories of issue over and over, but the logic of how a ticket should be triaged and routed often lives in people's heads — inconsistent, hard to scale, and lost when someone leaves.

What I Built

An analyzer that takes incoming support content and works out what it's actually about — categorizing the issue, assessing it, and applying consistent triage logic so every ticket is handled the same considered way regardless of who's on shift.

Architecture / How It Works

Incoming tickets are processed through an AI analysis layer that classifies and interprets them, applying defined logic to determine handling. Consistency comes from the same rules running every time, rather than relying on individual judgment under load.

01Customer Request
02Issue Extraction
03Root Cause
04Resolution
05Agent Actions
Investigation time
Consistent troubleshooting across teams
cognitive load for support engineers

Outcome

Triage that's consistent and scalable, capturing the reasoning that usually walks out the door — so the team's collective know-how becomes a system, not tribal memory.

Tech Stack

AIWorkflow Automation

Current Status

Working Prototype — Demo Available

Screenshots

Support Logic Analyzer screenshot 1
Support Logic Analyzer screenshot 2
Support Logic Analyzer screenshot 3

Demo

Let's fix what's actually broken.

I'm actively looking for remote AI engineering and operations roles where I can ship real automation, not just talk about it.

Multi-Agent Systems
Support Automation
Incident Response
Workflow Engineering

Built for impact, not just looks. · Michael Alusa Limisi · Nairobi, Kenya · © 2026