Agents only perform as well as the data underneath them. CASE is the order we build in: Context, Access, Structure, then Execution.
What it is. What your data actually means, stated plainly so an AI does not have to guess. Why one client gets net-45 and another pays up front. What “urgent” means in your shop. Without it, a model infers meaning from surrounding clues and runs on assumptions.
Why it matters. An agent without your context makes generic decisions: technically correct, commercially wrong. With it, the system decides the way you would.
What it is. Agents need a path to your data, its context, and its structure before they can act on it. That path is scoped credentials in your accounts, never ours. The invoicing agent reaches QuickBooks and job records; it cannot see payroll. Every action lands in a log you can read.
Why it matters. This is the difference between automation you can trust and a liability. Any mistake stays small, auditable, and yours to inspect. When we hand off, nothing routes through us.
What it is. A consistent shape for your data where today there is none. Not just merging five disagreeing spreadsheets, but deciding what a client, a job, and an invoice actually are, once. A number changes in one place or it does not change at all.
Why it matters. Agents acting on inconsistent data amplify the mess instead of clearing it. Clean structure makes everything above it dependable, and it pays for itself before the agents arrive.
What it is. The agents, the part you have probably heard most about. They draft invoices from time entries, chase overdue payments, flag renewals, prep briefs. You set what ships alone and what waits for your sign-off.
Why it matters. An agent can do a few things well on its own. To scale and stay reliable, it needs the three layers before it. Build those and the system also improves as the models do, with no rework.