Tablif TownWhat AI & Automation Consulting does for them
- Starts with a free 30-minute call on goals, constraints and fit, then turns that into a proposal, an assessment or a build.
- Checks the model choice your prototype made in a hurry against cost, compliance, latency and capability before it becomes permanent.
- Gets one real workflow into production through the AI Ops Starter: a model layer, one business integration and an actual use case.
- Leaves your engineers with working systems, documentation and deployment patterns, plus enough context to keep going alone.
Who it is not for
Not for teams that want an outside operator to run the system long term, since every engagement is scoped to end with a handoff to your own engineers.
Questions
Do you work with teams outside the US?
The listed setup is remote work with US-based clients, from the New York area. On-site or non-US work is not mentioned, so raise it on the first call if it applies to you.
Source: consultant.quinnfavo.comWhat happens after the free consultation?
If there is a fit, the next step is one of three things: a proposal, an architecture assessment, or an implementation engagement. Which one comes next gets decided on the call.
Source: consultant.quinnfavo.comIs the architecture assessment paid?
Yes. Detailed discovery, workshops, roadmaps and technical recommendations are paid consulting, often as a fixed-price assessment, and they happen before any building starts.
Source: consultant.quinnfavo.comAre you taking on new projects right now?
Yes, a limited number. Booking the free call is how you find out whether there is room for yours.
Source: consultant.quinnfavo.comWhat if our project doesn't match any of the packages?
That is what Custom AI Architecture is for. It covers mixed environments, migration decisions, platform hardening, and projects where the architecture direction is still unclear.
Source: consultant.quinnfavo.comCan the model run with no internet connection at all?
Yes. The LLM Private Cloud package covers private or air-gapped inference, with observability and RAG, on infrastructure your own team runs.
Source: consultant.quinnfavo.com