Most tools ask you to trust a resolution rate from someone else's help desk. Digby builds a track record on yours, one client and one category at a time, and shows you every number before you give him anything to touch.
Whether or not he is allowed to act, he records a private shadow decision: the action he would have proposed and a one-line plan. When your technician closes the ticket, a background sweep compares that guess with what actually happened and scores it.
Each ticket is scored once and never rescored. Scoring runs on the same cadence as the ticket poller, so there is nothing extra to babysit.
Once a category at a client has enough scored outcomes, Digby shows the track record in every note and folds it into his own confidence. A poor record makes him more cautious automatically. A good one is your evidence for the next rung.
Reads the ticket, looks things up the way a technician would, runs read-only diagnostics on the machine, and posts the write-up with cited evidence and both confidence scores. Nothing on the machine, the mailbox or the account changes. On the ticket itself he files a category and posts internal notes, both switchable off. This is where every pilot starts.
Suggests fixes as approval cards with a measured preview. A technician clicks Approve or Decline. This is where most desks run.
For a category whose scored track record at that client clears the bar — 20 checked tickets, 90% of them right — Digby can close the ticket himself, and only after the requester confirms the fix. Every other write still needs the click.
After every write-up a cheap model consolidates what was new into structured memory. The next ticket reads memory first and only pulls raw history when memory has no answer. Nothing crosses a client boundary.
Primary users, installed apps, known issues, patch windows, resolution patterns, golden configurations learned from working peers. Seeded from your docs and ticket history in one learning-mode run.
Process rules that apply everywhere: "check the switch before the PC on printer tickets", "never patch accounting firms during the first week of the month". Promoted from a client fact when a technician says so.
Who sits at which machine, who prefers a phone callback, who has asked about this before. Kept against the requester, so the next ticket from them starts where the last one ended.
Who wants the long version of the note, who never wants a scheduled reboot proposed after 4pm. Told to him in chat, and it shapes how he talks to that technician from then on.
"That's not the patch window." "This client doesn't use QuickBooks anymore." "Check the switch first on printer tickets." Digby retires the contradicted fact, records yours as technician-sourced, and replies with one sentence saying what changed.
When the same fix closes the same kind of ticket repeatedly, Digby drafts a runbook: the checks, the fix, the verify step, and the tickets it came from. A technician approves it. From then on it is a tool he can reach for, with its own used-and-succeeded history.
Every behavior change is listed with its source: a fact added or retired, a fact promoted to the whole MSP, a runbook drafted or approved, a dig-order pattern learned, a technician's correction. Grouped by client on the Intelligence page, and available as plain text for the weekly digest.
This is the answer to the question every service manager asks about an AI: "why did it do that?" With Digby the answer is a line in the ledger with a name and a ticket number on it.
Digby pages through a client's ticket history and documentation and seeds environment memory with sourced facts.
Repeated fixes in the history become draft runbooks, waiting for approval, each citing its tickets.
Tell him in plain English how your desk works — "check the switch before the PC on printer tickets" — and it becomes an MSP-wide rule with your name on it.
Two weeks on live tickets, writing up every one and fixing none, a scored report at the end, then you set the dial.
A shadow-mode pilot costs you nothing but a script-runner automation in your RMM. At the end you get a scored report per client and category.
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