Tablif TownThe story
Dana's Monday starts with a message from the security lead. A support automation, built quickly in the spring, has been sending ticket text to a model API. Nobody is sure whether that text includes customer account details, and nobody is sure how many other workflows do the same thing.
She counts what she knows. There is the support summarizer, a sales note writer, and a script someone in finance wired to a chat model. Each one was a good idea on its own. Together they are a list of prompts, tools and data flows that no single person has ever looked at.
On Tuesday she books the free 30-minute call with Quinn Favo. The call is about goals, constraints and fit, and they agree the first step is the AI Automation Audit, the package meant as a diagnostic entry point. It reviews the existing automation flows, the prompts, the tool integrations and the data boundaries.
The audit turns her rough list into a map. Two flows only ever see public text. One reaches into an internal system it does not need. The support summarizer is the one that matters: for that workload, keeping data inside company infrastructure is the requirement, not a nice idea.
That finding shapes the next step. For the support workload they look at the LLM Private Cloud package, private inference with observability and RAG that her own team can run. For the sales notes, a frontier API stays the smarter answer, and nobody pretends otherwise.
By the end of the week Dana has something she did not have on Monday: a written picture of where data goes, a plan she can explain to security, and documentation her engineers can keep up to date without calling anyone back in. The next time someone wants to wire a new tool to a model, the first question in the room is which data it will touch.
An illustrative story built only from features AI & Automation Consulting offers.
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