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The checklist
- Architecture written down: which models run where, and why each choice was made.
- Runbooks for the common failures, written for the engineer on call rather than the builder.
- Deployment patterns your team can repeat without the original author in the room.
- Observability in place: you can see latency, errors and what each request retrieved.
- A record of which tools each agent can call, and a way to audit those calls later.
- Data boundaries listed: what data each workflow may read, and where it is allowed to go.
- The routing rule between frontier APIs and private models, so moving a workload is a config change.
- A small set of test questions with expected answers, rerun after every change.
- Named owners on your side for each component, including who approves model or prompt changes.
- Costs written down per workload, so the next budget review starts from facts.
Questions
Do you also build mobile apps?
Yes. Quinn designs, builds and ships native Kotlin apps for Android and Wear OS on Google Play.
Source: consultant.quinnfavo.comCan a local model run on ordinary hardware?
Smaller ones can. There is an article on deploying Llama 3 8B on consumer hardware, where memory, context and concurrency are the main limits.
Source: consultant.quinnfavo.comCan you help with a migration between model setups?
Yes. Migration decisions and mixed environments fall under the Custom AI Architecture package.
Source: consultant.quinnfavo.comDoes a private deployment include monitoring?
Yes. The LLM Private Cloud package includes observability alongside inference and RAG.
Source: consultant.quinnfavo.comAre the agents tied to one model vendor?
No. The agents are model-agnostic, so the model behind them can change without rebuilding the tool access.
Source: consultant.quinnfavo.comCan we see which tools an agent used?
Yes. Agent Mesh is built around auditable tool access across cloud, infrastructure and internal systems.
Source: consultant.quinnfavo.com