Local AI Agent Troubleshooter

Emmanuel Ezeokeke

Emmanuel Ezeokeke

Goal

I built a privacy-first AI assistant that automatically resolves IT errors by extracting information from screenshots, searching knowledge bases, and generating solutions—while protecting sensitive client data.

Challenges

Manual troubleshooting delays: IT support teams spent hours searching through documentation for error solutions
Screenshot-based tickets: Users submitted error screenshots that required manual text extraction and analysis
Privacy risks: Existing tools sent sensitive client data to external APIs, violating compliance requirements
Knowledge fragmentation: Error solutions scattered across multiple PDFs, databases, and documentation sources
Escalation bottlenecks: No automated way to draft escalation tickets when solutions weren't found

Solution & Approach

Developed ARES (Automated Resolution & Escalation System) using LangGraph workflow orchestration with:
Vision LLM OCR: Ollama-based text extraction from error screenshots
Dual RAG System: Separate vector databases for errors (Oracle, MySQL, MITRE) and general knowledge
Smart PII Detection: Pattern-based filtering that distinguishes error codes from sensitive data
Interactive Workflow: LangGraph interrupts for user approval with automatic escalation on rejection
Advanced Document Processing: Docling v2 for PDFs with table/chart recognition
Tech Stack: Python, FastAPI, LangGraph, ChromaDB, Ollama (gemma2:4b), Qwen embeddings, Streamlit

Key Features

Privacy-First: Rejects requests containing client information automatically
Multi-Format Support: Processes PDFs, images, CSV, JSON, Markdown, HTML
GPU Acceleration: CUDA/MPS support for faster embeddings and inference
Stateful Workflows: Checkpointed execution with interrupt/resume capability
Comprehensive Testing: 80% code coverage with pytest suite
Production Ready: Structured logging, metrics tracking, error handling

Results

6-node workflow with conditional routing and human-in-the-loop approval
Dual knowledge bases indexed from 13 documents (6 error docs + 7 knowledge docs)
Real-time PII detection with 95% confidence scoring
Automated escalation drafting when solutions aren't found
25 test images validated for OCR accuracy
Ready to Build Your AI Agent?
I specialize in developing production-grade AI agents with LangGraph, RAG systems, and workflow orchestration. Whether you need document processing, intelligent automation, or privacy-focused AI solutions, I deliver scalable systems that work.
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Posted Oct 30, 2025

Built ARES, an AI assistant for IT error resolution with privacy-first features.