fix: align project naming to AIOps Lab and add commercialization roadmap

CONTRIBUTING.md

@@ -1,6 +1,6 @@
 # Contributing
 
-Thanks for helping improve `ai-infra-starter-kit`.
+Thanks for helping improve AIOps Lab.
 
 The main rule is simple: keep the project beginner-friendly without making it shallow. A new feature should teach a real AI infrastructure concept at the right time.
 

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2026 ai-infra-starter-kit contributors
+Copyright (c) 2026 AIOps Lab contributors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,7 +2,7 @@
 
 Simple first. Production-minded always.
 
-`ai-infra-starter-kit` is a practical learning lab for AI infrastructure. It starts with a normal web service, adds logs and metrics, then uses an AI SRE Assistant to explain what is happening operationally.
+AIOps Lab is a practical learning lab for AI infrastructure. It starts with a normal web service, adds logs and metrics, then uses an AI SRE Assistant to explain what is happening operationally.
 
 The first version runs on a normal laptop. No GPU, Kubernetes, vLLM, Triton, Ray, KServe, or full MLOps platform is required on Day 1.
 
@@ -41,8 +41,8 @@ The AI assistant does not need an LLM key to work. If no provider is configured,
 ## Quickstart
 
 ```bash
-git clone https://github.com/your-username/ai-infra-starter-kit.git
-cd ai-infra-starter-kit
+git clone https://github.com/utkarshp845/AIOps-Lab.git
+cd AIOps-Lab
 cp .env.example .env
 make up
 make test
@@ -97,6 +97,8 @@ For the advanced model serving decision framework and commercialization path, se
 
 For the Week 4 release gates, readiness verdict, and next-stage priorities, see `docs/20-production-readiness-review.md`.
 
+For the phased commercialization roadmap with milestones and success metrics, see `docs/21-commercialization-roadmap.md`.
+
 Ask the assistant directly:
 
 ```bash
@@ -124,7 +126,7 @@ curl -s -X POST http://localhost:8001/summarize-incident \
 ## Repository Structure
 
 ```text
-ai-infra-starter-kit/
+AIOps-Lab/
   apps/
     demo-service/        # FastAPI app that emits health, failure, latency, logs, and metrics
     ai-sre-assistant/    # FastAPI app and CLI that analyze demo-service logs
@@ -141,6 +143,7 @@ ai-infra-starter-kit/
     18-production-observability.md
     19-advanced-model-serving-roadmap.md
     20-production-readiness-review.md
+    21-commercialization-roadmap.md
   infra/                 # Docker, Kubernetes, and Terraform starter notes
     k8s/                 # kind-first Kubernetes manifests and walkthrough
   scripts/               # Local traffic and log helper scripts
@@ -190,6 +193,8 @@ The open-source project makes the core learning path, safety controls, and evalu
 
 The future paid opportunity is the recurring team workflow around those foundations: hosted evaluation history, private incident datasets, release gates, model and provider comparisons, regression alerts, collaboration, and audit-ready exports. The product value is not simply generating an incident summary. It is helping teams prove that their operational assistant stays useful, safe, private, and cost-aware as their systems change.
 
+The sequenced plan — audience first, wedge product with design partners second, paid team tier third — lives in `docs/21-commercialization-roadmap.md` with per-phase milestones, success metrics, and exit criteria.
+
 ## Lessons Learned
 
 Day 1 lessons are intentionally simple:

SECURITY.md

@@ -1,6 +1,6 @@
 # Security Policy
 
-`ai-infra-starter-kit` is a learning project, but security issues still matter.
+AIOps Lab is a learning project, but security issues still matter.
 
 This repo intentionally starts local and simple. Please do not treat the default Docker Compose or kind setup as production-ready.
 

docker-compose.yml

@@ -1,10 +1,10 @@
-name: ai-infra-starter-kit
+name: aiops-lab
 
 services:
   demo-service:
     build:
       context: ./apps/demo-service
-    container_name: ai-infra-demo-service
+    container_name: aiops-demo-service
     ports:
       - "8000:8000"
     environment:
@@ -20,7 +20,7 @@ services:
   ai-sre-assistant:
     build:
       context: ./apps/ai-sre-assistant
-    container_name: ai-infra-ai-sre-assistant
+    container_name: aiops-ai-sre-assistant
     ports:
       - "8001:8001"
     environment:
@@ -46,11 +46,11 @@ services:
   # Optional local model provider for later experiments.
   # 1. Uncomment this service.
   # 2. Set LLM_PROVIDER=ollama and OPENAI_BASE_URL=http://ollama:11434/v1 in .env.
-  # 3. Pull a model with: docker exec -it ai-infra-ollama ollama pull llama3.1
+  # 3. Pull a model with: docker exec -it aiops-ollama ollama pull llama3.1
   #
   # ollama:
   #   image: ollama/ollama:latest
-  #   container_name: ai-infra-ollama
+  #   container_name: aiops-ollama
   #   ports:
   #     - "11434:11434"
   #   volumes:

docs/00-overview.md

@@ -1,6 +1,6 @@
 # Overview
 
-`ai-infra-starter-kit` teaches AI infrastructure by starting with the parts every production engineer already understands: services, logs, health checks, metrics, containers, and operational debugging.
+AIOps Lab teaches AI infrastructure by starting with the parts every production engineer already understands: services, logs, health checks, metrics, containers, and operational debugging.
 
 The AI part is introduced as an assistant that observes a service, not as a giant platform on Day 1.
 

docs/09-roadmap.md

@@ -35,6 +35,10 @@
 - Optional advanced serving roadmap: vLLM, Triton, Ray, KServe, and GPU scheduling.
 - Production-readiness review with local and CI release gates.
 
+## Commercialization
+
+The phased plan for turning the lab into a monetized product — audience, wedge product, paid team tier — is in `21-commercialization-roadmap.md`.
+
 ## Later
 
 - OpenTelemetry collector.

docs/10-kubernetes-operations-runbook.md

@@ -17,23 +17,23 @@ This runbook assumes you already created the kind cluster, loaded local images,
 All Kubernetes resources for this project run in one namespace:
 
 ```bash
-kubectl get all -n ai-infra-starter-kit
+kubectl get all -n aiops-lab
 ```
 
 For a shorter local session, you can set the namespace on your current context:
 
 ```bash
-kubectl config set-context --current --namespace=ai-infra-starter-kit
+kubectl config set-context --current --namespace=aiops-lab
 ```
 
-The rest of this guide keeps `-n ai-infra-starter-kit` in commands so each example is explicit and copy-paste friendly.
+The rest of this guide keeps `-n aiops-lab` in commands so each example is explicit and copy-paste friendly.
 
 ## Quick Status Check
 
 Start with the cluster objects that explain most local issues:
 
 ```bash
-kubectl get deployments,pods,svc,pvc -n ai-infra-starter-kit
+kubectl get deployments,pods,svc,pvc -n aiops-lab
 ```
 
 What you want to see:
@@ -47,20 +47,20 @@ What you want to see:
 Then confirm rollout status:
 
 ```bash
-kubectl rollout status deployment/demo-service -n ai-infra-starter-kit
-kubectl rollout status deployment/ai-sre-assistant -n ai-infra-starter-kit
+kubectl rollout status deployment/demo-service -n aiops-lab
+kubectl rollout status deployment/ai-sre-assistant -n aiops-lab
 ```
 
 ## Health, Readiness, And Metrics
 
 Port-forward both services in separate terminals:
 
 ```bash
-kubectl port-forward svc/demo-service 8000:8000 -n ai-infra-starter-kit
+kubectl port-forward svc/demo-service 8000:8000 -n aiops-lab
 ```
 
 ```bash
-kubectl port-forward svc/ai-sre-assistant 8001:8001 -n ai-infra-starter-kit
+kubectl port-forward svc/ai-sre-assistant 8001:8001 -n aiops-lab
 ```
 
 Check the app-level signals:
@@ -117,14 +117,14 @@ For an end-to-end incident walkthrough that uses these signals together, see `13
 View recent logs from each Deployment:
 
 ```bash
-kubectl logs deployment/demo-service -n ai-infra-starter-kit --tail=50
-kubectl logs deployment/ai-sre-assistant -n ai-infra-starter-kit --tail=50
+kubectl logs deployment/demo-service -n aiops-lab --tail=50
+kubectl logs deployment/ai-sre-assistant -n aiops-lab --tail=50
 ```
 
 Follow logs while generating traffic:
 
 ```bash
-kubectl logs deployment/demo-service -n ai-infra-starter-kit --follow
+kubectl logs deployment/demo-service -n aiops-lab --follow
 ```
 
 For this learning lab, the assistant also reads the shared file at:
@@ -142,14 +142,14 @@ Services give pods stable names inside the cluster.
 Check service objects:
 
 ```bash
-kubectl get svc -n ai-infra-starter-kit
+kubectl get svc -n aiops-lab
 ```
 
 Check service endpoints:
 
 ```bash
-kubectl get endpoints demo-service -n ai-infra-starter-kit
-kubectl get endpoints ai-sre-assistant -n ai-infra-starter-kit
+kubectl get endpoints demo-service -n aiops-lab
+kubectl get endpoints ai-sre-assistant -n aiops-lab
 ```
 
 If a Service has no endpoints, Kubernetes has not found any ready pods behind it. Check pod labels, readiness probes, and pod status.
@@ -159,13 +159,13 @@ If a Service has no endpoints, Kubernetes has not found any ready pods behind it
 Normal configuration lives in the ConfigMap. For the full config and secrets walkthrough, see `11-kubernetes-config-and-secrets.md`:
 
 ```bash
-kubectl describe configmap ai-infra-starter-kit-config -n ai-infra-starter-kit
+kubectl describe configmap aiops-lab-config -n aiops-lab
 ```
 
 Sensitive configuration belongs in a Secret:
 
 ```bash
-kubectl get secret ai-sre-assistant-secrets -n ai-infra-starter-kit
+kubectl get secret ai-sre-assistant-secrets -n aiops-lab
 ```
 
 Do not paste real API keys into screenshots, tweets, issues, or pull requests.
@@ -177,13 +177,13 @@ The local kind setup uses a shared PersistentVolumeClaim so the assistant can re
 Check the claim:
 
 ```bash
-kubectl get pvc -n ai-infra-starter-kit
+kubectl get pvc -n aiops-lab
 ```
 
 Describe it if it is not `Bound`:
 
 ```bash
-kubectl describe pvc shared-logs -n ai-infra-starter-kit
+kubectl describe pvc shared-logs -n aiops-lab
 ```
 
 This is a learning bridge from Docker Compose to Kubernetes. It is not the recommended production logging architecture.
@@ -197,18 +197,18 @@ Likely cause: kind cannot see the local Docker image.
 Check:
 
 ```bash
-kubectl describe pod -l app.kubernetes.io/name=demo-service -n ai-infra-starter-kit
+kubectl describe pod -l app.kubernetes.io/name=demo-service -n aiops-lab
 ```
 
 Fix:
 
 ```bash
-docker build -t ai-infra-starter-kit/demo-service:local apps/demo-service
-docker build -t ai-infra-starter-kit/ai-sre-assistant:local apps/ai-sre-assistant
-kind load docker-image ai-infra-starter-kit/demo-service:local --name ai-infra-starter-kit
-kind load docker-image ai-infra-starter-kit/ai-sre-assistant:local --name ai-infra-starter-kit
-kubectl rollout restart deployment/demo-service -n ai-infra-starter-kit
-kubectl rollout restart deployment/ai-sre-assistant -n ai-infra-starter-kit
+docker build -t aiops-lab/demo-service:local apps/demo-service
+docker build -t aiops-lab/ai-sre-assistant:local apps/ai-sre-assistant
+kind load docker-image aiops-lab/demo-service:local --name aiops-lab
+kind load docker-image aiops-lab/ai-sre-assistant:local --name aiops-lab
+kubectl rollout restart deployment/demo-service -n aiops-lab
+kubectl rollout restart deployment/ai-sre-assistant -n aiops-lab
 ```
 
 ### Pods Are Running But Not Ready
@@ -218,8 +218,8 @@ Likely cause: readiness probes are failing or the app is not listening on the ex
 Check:
 
 ```bash
-kubectl describe pod -l app.kubernetes.io/name=demo-service -n ai-infra-starter-kit
-kubectl logs deployment/demo-service -n ai-infra-starter-kit --tail=100
+kubectl describe pod -l app.kubernetes.io/name=demo-service -n aiops-lab
+kubectl logs deployment/demo-service -n aiops-lab --tail=100
 ```
 
 Look for failed readiness probe events, startup errors, or port mismatches.
@@ -236,10 +236,10 @@ Likely causes:
 Check:
 
 ```bash
-kubectl get pvc -n ai-infra-starter-kit
-kubectl describe configmap ai-infra-starter-kit-config -n ai-infra-starter-kit
-kubectl logs deployment/demo-service -n ai-infra-starter-kit --tail=50
-kubectl logs deployment/ai-sre-assistant -n ai-infra-starter-kit --tail=50
+kubectl get pvc -n aiops-lab
+kubectl describe configmap aiops-lab-config -n aiops-lab
+kubectl logs deployment/demo-service -n aiops-lab --tail=50
+kubectl logs deployment/ai-sre-assistant -n aiops-lab --tail=50
 ```
 
 Then generate traffic again:
@@ -259,8 +259,8 @@ Likely causes:
 Check:
 
 ```bash
-kubectl get endpoints demo-service -n ai-infra-starter-kit
-kubectl describe configmap ai-infra-starter-kit-config -n ai-infra-starter-kit
+kubectl get endpoints demo-service -n aiops-lab
+kubectl describe configmap aiops-lab-config -n aiops-lab
 curl http://localhost:8000/metrics
 ```
 
@@ -278,7 +278,7 @@ Check:
 
 ```bash
 kubectl get pv
-kubectl describe pvc shared-logs -n ai-infra-starter-kit
+kubectl describe pvc shared-logs -n aiops-lab
 ```
 
 For this repo, `infra/k8s/storage.yaml` includes a kind-friendly local PersistentVolume. Reapply it if needed:
@@ -298,14 +298,14 @@ Likely causes:
 Check:
 
 ```bash
-kubectl get svc -n ai-infra-starter-kit
+kubectl get svc -n aiops-lab
 ```
 
 Use different local ports if needed:
 
 ```bash
-kubectl port-forward svc/demo-service 18000:8000 -n ai-infra-starter-kit
-kubectl port-forward svc/ai-sre-assistant 18001:8001 -n ai-infra-starter-kit
+kubectl port-forward svc/demo-service 18000:8000 -n aiops-lab
+kubectl port-forward svc/ai-sre-assistant 18001:8001 -n aiops-lab
 ```
 
 Then call:
@@ -319,7 +319,7 @@ curl http://localhost:18001/health
 
 Use this order when something is wrong:
 
-1. `kubectl get deployments,pods,svc,pvc -n ai-infra-starter-kit`
+1. `kubectl get deployments,pods,svc,pvc -n aiops-lab`
 2. `kubectl describe pod ...`
 3. `kubectl logs deployment/...`
 4. app health endpoints through port-forward
@@ -333,8 +333,8 @@ Start with read-only inspection. Restart or delete resources only after the evid
 
 Useful screenshots for a build-in-public update:
 
-- `kubectl get deployments,pods,svc,pvc -n ai-infra-starter-kit`
-- `kubectl rollout status deployment/demo-service -n ai-infra-starter-kit`
+- `kubectl get deployments,pods,svc,pvc -n aiops-lab`
+- `kubectl rollout status deployment/demo-service -n aiops-lab`
 - `curl http://localhost:8000/health` and `curl http://localhost:8000/metrics`
 - `POST /analyze/metrics` response from the assistant
 - `kubectl logs deployment/demo-service --tail=20`