# Private delivery artifact list: notes from a Shenzhen bench

# Making “Private delivery artifact list” work inside a company

Runbook, eval results, hardware BOM, and access matrix. Internal assistants built on local models succeed when they are treated like any other production service: owned, monitored and documented. They fail when they remain a demo that slowly turns into a dependency.

# Ownership

Name one owner for the service and one backup. The owner approves model changes, maintains the runbook and answers questions from security and finance. Without a name, issues bounce between IT and the team that started the pilot.

# Boundaries

Write down what the assistant does and does not do. Summaries, drafting and search over approved documents are typical. Posting journal entries, approving payments, interpreting contracts or giving medical and legal advice should stay with people. When a request crosses a boundary, the assistant should say so plainly and offer the manual path.

# Security basics

• Separate system instructions from user input and from retrieved text.
• Treat tool results and uploaded files as untrusted.
• Keep audit logs in a place users cannot edit.
• Review findings from internal testing with named owners and deadlines.

# Monitoring

Watch the things that cause outages: GPU temperature, disk space, process crashes and queue length. Alert on those, not on every individual error. Publish a simple status page so colleagues know when the service is degraded.

# Reviews

# Questions leadership will ask

• What data does the system see, and where is it stored?
• Who can use it, and how is access removed when someone leaves?
• What happens when the answer is wrong, and who is responsible?
• What does it cost per month, including power and staff time?
• How do we switch it off quickly if something goes wrong?

Prepare short, factual answers before the first meeting. A one-page summary with an owner, a data policy and a kill switch usually moves the conversation from “is it safe” to “where do we start”.

More on this topic: MicroAI One software. See also private deployment services.

# Bottom line

Local AI rewards patience and record keeping more than raw spending. Decide what the system must do, measure it on your own hardware and documents, write down every change with a date and an owner, and review the setup after each significant upgrade. Small, documented steps are easier to debug, easier to hand over and easier to defend when someone asks why a particular choice was made. When in doubt, test on a smaller scale first and expand only when the evidence supports it.

# Quick reminders

• Treat retrieved text and uploaded files as untrusted.
• Publish what the assistant will refuse to do.
• Photograph cable routing before closing the case.
• Read the model licence before commercial use.

Published by MicroAI One (本地AI 1号网), a Shenzhen team working on local LLM hardware, software and deployment.