Guide

How to prepare for a free AI architecture call

A 30-minute call goes fast, and most of it is lost if you spend it explaining basics. These steps help you arrive with what Quinn Favo needs to judge fit and suggest the next step.

1 min readWritten from consultant.quinnfavo.com✓ Approved by the founder · 4 Nov 2026
On this page
  1. Step by step
  2. Questions

Step by step

  1. Write one paragraph on what you want the AI system to do and for whom.Screen: Consulting home page with the Book a call button
  2. List your constraints, such as compliance rules, data that cannot leave your network and any latency needs.Screen: Who it's for section
  3. Note whether you are leaning toward frontier APIs, a private model or a mix, even if you are unsure.Screen: Frontier, private and hybrid overview
  4. Run your rough numbers through the GPU vs API cost calculator on the site.Screen: GPU vs API cost calculator
  5. Look at the packages and pick the one closest to your risk today.Screen: Package details page
  6. Book the free 30-minute consultation from the site.Screen: Booking page
  7. Expect the outcome to be a next step, such as a proposal, an assessment or an implementation engagement.Screen: Process section

Questions

Do you work with Kubernetes?

Yes. Private and hybrid deployments range from a single GPU box to a Kubernetes cluster.

Source: consultant.quinnfavo.com
Have you done this in a regulated industry?

Yes. One case study is an air-gapped LLM deployment for a financial services organization that could not send proprietary data to commercial APIs.

Source: consultant.quinnfavo.com
Can I compare GPU and API costs before a call?

Yes. The site has a GPU vs API cost calculator you can try first.

Source: consultant.quinnfavo.com
Do you work on RAG and MCP setups?

Yes. DevOps, MCP, RAG and workflow automation are part of the tooling work.

Source: consultant.quinnfavo.com
Can you set up a model that runs fully on our own infrastructure?

Yes. The LLM Private Cloud package covers private or air-gapped inference, with observability and RAG your team can operate on its own.

Source: consultant.quinnfavo.com
What is the Agent Mesh package?

Agents that are not tied to one model, with auditable access to tools across cloud, infrastructure and internal systems, built on MCP patterns.

Source: consultant.quinnfavo.com

Designs and builds private, frontier API or hybrid LLM infrastructure for US engineering teams, then hands it over with docs they can run.