The engineering library
Practical guides for the people deciding what to build and the engineers making it work. Explore the tradeoffs, reference designs, and evaluation methods.
Private AI · Fundamentals
A practical starting point for deciding where AI should run, who should control it, and whether a private deployment fits your business.
Read the guide : What is private AI, and when does it make sense?Planning · Practical guide
Define a first project around observable business value, representative examples, and a clear owner of the result.
Read the guide : Choose an AI workflow worth implementingPrivate AI · Fundamentals
Separate promises about data movement, data storage, and network transport before choosing an AI architecture.
Read the guide : Zero egress, zero retention, and private endpointsPrivate AI · Technical guide
Design the complete inference boundary, including retrieval, identity, logs, model preparation, and maintenance.
Read the guide : A reference architecture for zero-egress LLMsEngineering · Technical guide
Build an acceptance test that exercises real work, attempts prohibited connections, and records exactly what was verified.
Read the guide : How to verify a zero-egress AI environmentEngineering · Technical guide
Design ingestion, permissions, retrieval, citations, and document updates as one system around a locally served model.
Read the guide : Build a private document assistant with RAGEngineering · Practical guide
Compare models using a stable task set, useful-answer criteria, realistic load, and explicit operating constraints.
Read the guide : Evaluate private LLMs on the work that mattersPlanning · Practical guide
Compare the whole operating model: hardware, utilization, integration, support, review effort, and recovery capacity.
Read the guide : What does private LLM hosting actually cost?Engineering · Technical guide
Plan ownership, local observability, release bundles, updates, backups, and recovery before the system becomes part of daily work.
Read the guide : Operate private AI after the first successful demoEngineering · Technical guide
Separate reasoning from tools, enforce permissions outside the model, and define the data allowed to cross each boundary.
Read the guide : Local inference does not make every agent action privateHow to use the library
Deciding whether private AI fits? Start with the fundamentals and the differences between privacy, retention, and egress.
Designing a system? Follow the reference architecture, then work through verification and operations.
Making the business case? Read about choosing a workflow and comparing the full cost.
Guides are educational reference material. Examples state their assumptions; they are not client case studies. Each article includes a date and relevant primary sources where it describes platform behavior.
Start a conversation
Use the guides to explore the options. Bring a workflow or architecture question when you’re ready to discuss implementation.
