Building IncidentCopilot: Establishing a Local-First AI DevOps Development Foundation

A developer has completed Milestone 1 of IncidentCopilot, an AI DevOps incident investigation project, establishing a local-first development foundation with a FastAPI backend, React/TypeScript frontend, PostgreSQL, Qdrant, and Ollama running via Docker Compose. The milestone deliberately excludes LLM integration, log ingestion, and RAG, following the stated principle that "the LLM will sit after the deterministic evidence pipeline.

Project: IncidentCopilot — AI DevOps Incident Investigation Milestone: 1 — Repository & Local Development Foundation Status: ✅ Completed 🎯 Why Start With the Foundation? When building an AI-powered DevOps system, it’s tempting to jump straight into the LLM. For IncidentCopilot, I deliberately chose not to. Evidence first. AI second. Human in the loop. The AI should reason over verified evidence, not replace deterministic systems like parsing, normalization, persistence, or correlation. So Milestone 1 focused on: - Repository structure - Local dev environment - Backend & frontend foundations - Config management - Testing setup - Docker & Compose - Documentation & reproducibility 🖥️ Local-First Decision IncidentCopilot is intentionally local-first . No reliance on: - AWS / Azure / GCP - Paid APIs - Proprietary SaaS infrastructure Instead, the stack runs via Docker Compose : - FastAPI - PostgreSQL - Qdrant - Ollama - React 📂 Repository Structure Backend packages were defined but left intentionally empty — establishing architectural direction without premature implementation. ⚙️ Backend Foundation - FastAPI app with two endpoints: - Config management via pydantic-settings - Testing with Pytest + FastAPI’s TestClient - Dockerized backend minimal container, no DB/AI yet 🎨 Frontend Foundation - React + TypeScript + Vite + Tailwind CSS + Lucide icons - Minimal shell: IncidentCopilot — AI DevOps Incident Investigation - Cleaned unused Vite starter files - Dockerized frontend with Node-based build image 🛠️ Real Problems & Fixes - Node.js mismatch: upgraded from v20 → v24 for Vite - Docker Desktop: CLI installed but engine not running — fixed by starting Docker Desktop - Windows make : used mingw32-make instead of GNU make - Git hygiene: fixed invalid UTF-8 README + refined .gitignore ✅ Verification - Git hygiene → clean - Backend tests → 1 passed - Frontend lint → 0 errors - Frontend build → ✓ built - Docker Compose config → valid - Backend & frontend containers → running locally 🧩 What We Didn’t Build Yet Milestone 1 deliberately excluded: - PostgreSQL models - Log ingestion APIs - Parsers Nginx, Kubernetes, Docker, GitHub Actions - Normalization & correlation - Qdrant + RAG integration - Ollama integration - Structured AI diagnosis - Full incident dashboard These belong to future milestones. 🏗️ Architecture Principle The LLM will sit after the deterministic evidence pipeline. 📌 Key Takeaways 1. Foundation work = real development 2. Verification assumptions 3. Starter templates should be questioned 4. Local-first changes dev strategy 5. AI shouldn’t be the first thing we build 🔮 What’s Next? Milestone 2 — FastAPI Foundation + PostgreSQL Moving toward: 🏁 Final Thoughts IncidentCopilot is still at the beginning. No AI diagnosis yet. No RAG. No ingestion pipeline. And that’s okay. Milestone 1 established the engineering environment needed to build those capabilities correctly. The project now has: - Structured monorepo - FastAPI + React/TypeScript - Tailwind CSS - Config management - Testing - Docker + Compose - Verified local workflow Most importantly: Build the evidence pipeline first. Let AI reason over verified evidence later. 🔗 GitHub: https://www.github.com/richardatodo/incidentcopilot https://www.github.com/richardatodo/incidentcopilot ➡️ Next: Milestone 2 — FastAPI + PostgreSQL