McPherson AI helps restaurant managers reduce missed handoffs, recurring follow-up, labor and prep drift, and the operational details that keep living in one manager's head.
The first step is not asking an operator to trust a full AI deployment. The first step is a paid diagnostic that maps one or two real workflow leaks, defines what should stay human-owned, and scopes the smallest useful pilot.
A full AI pilot is a big first step for a busy restaurant operator. The Operator Diagnostic is a smaller, paid engagement: I spend time inside your operation, talk with the people running the shift, and deliver a written assessment of where AI support actually fits — and where it should not be used yet.
You keep the assessment either way. If there is fit and you move to a Founder Pilot, the full diagnostic fee credits toward setup.
Daily monitoring, weekly diagnostics, and the kind of follow-through most stores never get to because the rush always wins.
The goal is not to promise a perfect ROI number before data exists. The goal is to measure whether the system returns manager time and reduces operational friction.
McPherson AI exists because restaurant operators are asked to solve the same workflow problems manually, under time pressure, with scattered context and delayed feedback.
The tools I am building are designed to reduce missed handoffs, surface recurring issues earlier, and give managers back time they lose to manual follow-up.
This is product shaped by operational understanding — practical workflow logic turned into usable systems for managers, shift leaders, and local operators.
McPherson AI runs on Anthropic's Claude — chosen for its handling of business data, output quality, and structured operator workflows. Each client deployment runs on its own isolated infrastructure, so store data stays scoped to the store.
The agent layer, memory architecture, skill design, and operational guardrails are McPherson AI's work. Anthropic's models provide the inference layer; the operator-specific intelligence — what the system knows about how a store actually runs — is built and maintained by us.
The system is engineered for cost discipline. Prompt caching, structured compression, and a multi-agent architecture keep per-client infrastructure costs predictable, which is what allows founder pilot pricing to stay flat instead of metered.
Anthropic states that, by default, inputs and outputs from commercial products such as the Anthropic API are not used to train its models. Anthropic also states that standard API inputs and outputs are generally deleted from its backend within 30 days, subject to listed exceptions. McPherson AI separately scopes each client deployment through isolated infrastructure, workspace, memory, secrets, and messaging bindings.
McPherson AI publishes its architecture, governance model, and category thesis as public work. The focus is agent infrastructure for small business operations — workflows, memory, and operator judgment.
If your store is dealing with missed handoffs, labor/prep drift, food cost visibility, audit scramble, or follow-up that keeps living in the manager's head, start with the Operator Diagnostic.