Industry Case Study
Secure, low-cost LLM support chatbots for small business deployments
Explore the Architecture, Platform design, Delivery Pipeline, and Findings from a k3s-based edge AI private cloud with DGX unified memory and multi-tenant isolation.
Industry Case Focus
Chatbot workflow showing retrieval, guardrails, and response assembly for industry support teams.
Architecture
Edge AI private cloud.
Edge AI computing cluster built on k3s with unified DGX nodes.
Platform
Architecture highlights
Distributed k3s clusters
Lightweight Kubernetes nodes span heterogeneous, low-cost machines while maintaining a consistent control plane.
Encrypted overlay network
Overlay routing connects nodes securely, enabling pooled compute without centralizing all infrastructure.
Multi-tenant isolation
Container-based tenancy boundaries and per-tenant data access controls keep client data separated and auditable.
No-code workflow
Small business operators can deploy and configure chatbots without dedicated ML engineering teams.
Delivery Pipeline
From code submission to live deployment
Feature branches land in the mainline.
Linting, tests, and security scans.
Semantic tagging for traceability.
Container build and registry publish.
Automated rollout to edge AI nodes.
Findings
Real-world deployment findings
E-commerce validation
The platform is tested in a live customer support setting, demonstrating stable performance under realistic traffic.
Operational constraints
Resource pooling and lightweight clusters deliver lower total cost while preserving reliability.
Security impact
Practical defense layers reduce prompt injection exposure without retraining or specialized hardware.