Offering Breakdown: Local LLM Deployment Packages¶

Version: 1.01.26
Audience: Consultant / Sales / Architect

1. Package Names (Client-Facing)¶

You can present these SOPs as service “packages”:

Package A — Local Chat Companion
→ Based on SOP #3 (LM Studio) or SOP #5 (Goose Standalone)
→ Single-user, reasoning/chat, no automation, minimal complexity.
Package B — Containerized Assistant with UI
→ Based on SOP #1 (LLM Container + Goose UI)
→ Local model, Docker isolation, Goose desktop UI.
Package C — High-Privacy CLI Assistant
→ Based on SOP #2 (Terminal-Only LLM Container)
→ For security-conscious or technical users, minimal attack surface.
Package D — Automated AI Workflows
→ Based on SOP #4 (Goose + n8n + LLM+Agent)
→ Local AI that runs workflows on schedules or triggers.

“You get a local AI assistant with a GUI plus container isolation for better control.”
Designed for: users who want UI comfort and technical teams who care about isolation.
Requires Docker Desktop (Windows) or Docker Engine (Linux).

“You get a local AI that runs in a terminal, with no extra apps or telemetry surfaces.”
Designed for: privacy-sensitive or regulated environments.
Best where technicians are comfortable with CLI tools.

“You get an AI system that can run tasks automatically on a schedule or trigger.”
Designed for: teams who need recurring tasks (e.g., organize notes, summarize logs).
Uses n8n for workflow logic, Goose for ad-hoc interaction, and a containerized agent.

From Package A → B
When client later wants more isolation, containerized deployment, or more advanced control.
From Package A/B → D
When client asks: “Can this automatically run every night / every 4 hours?”
From Any → C (High Privacy)
When client raises: secret-class data, doctor–patient, or lawyer–client confidentiality concerns.

Each package maps 1:1 to an SOP: