Help running AI on your own hardware.

The problem

Hosted LLM APIs are powerful, but for regulated teams they create a gap your compliance team can't close: privileged material flowing out to a third-party inference endpoint, opaque retention, shared infrastructure, no audit trail. Most enterprise AI stops at "summarize this email" because anything deeper runs into legal, InfoSec, or both.

What you get

A working private AI system running on your infrastructure, with the durability features most teams try to bolt on later — baked in from day one:

• Persistent, searchable memory. Your AI remembers across sessions and projects, with clean isolation between workstreams.
• Real tool use. The model can read from and write to your systems via the Model Context Protocol, with explicit permission scopes per tool.
• Governance & audit. Every action passes through a policy engine and lands in an append-only audit ledger. You can answer the question "what did the AI actually do yesterday?" in one query.
• Multi-tenant from day one. Matters, clients, or departments get isolated memory spaces. Cross-contamination is not a configuration — it's impossible by design.

How I approach it

The first week is listening. I want to understand what your team actually does hour-by-hour before recommending any architecture. Most private-AI projects fail because they deploy a chatbot when what the team needed was a retrieval-and-drafting pipeline that quietly saves two hours a day.

From there, most engagements follow a shape: two weeks of deployment and integration, one week of operator training, 30 days of hands-on support as real work flows through the system. By week 8 you should be running independently, with documentation good enough to hand to your own ops team.