One real example,
start to finish.
No theory. Here's exactly what happens when a business hands us one automation to check.
Meet the customer: a plumbing-supply company. Their AI automation creates and emails the invoice whenever a sales rep closes a deal. It saves hours. He isn't sure it is always right, and he has no way to check.
Step 1. Write down what “right” means
One sentence, in the owner's words: “Every invoice must match the quote the customer accepted, right amount, right customer, sent once.” Sounds obvious. Nobody ever writes it down. That sentence becomes the standard everything is tested against.
Step 2. We design the trick questions
This is the skill you're paying for. Automations fail in patterns: two customers with nearly identical names; a deal edited after approval; a discount typed as 100 instead of 10; a network hiccup that makes the bot retry, and bill twice. We write roughly ten scenarios like these, as fake customers and fake deals, built so we already know exactly what the correct invoice should be for each one. Like handing a student a math test where you hold the answer key.
Step 3. Your team runs the drill in a sandbox
You get the kit: a plain-English test plan plus the fake-data files. Your team (or the agency that built your automation) feeds them through the workflow in test mode, the practice copy every platform has. A fire drill: real behavior, nothing real at stake.
What we never ask for: passwords, logins, system access, customer data, or files from your real records. Every record in the test is one we invented, so there is nothing sensitive to leak, and nothing for your IT person to approve. If a company selling “AI verification” asks for your credentials, that's the company to worry about.
Step 4. We grade it against the answer key
You send back what the bot produced, the invoices from the fake runs, the records it changed, the log export. We line them up, scenario by scenario: number 4 was supposed to bill $486, it billed $4,860. Number 7 was supposed to send once, it sent twice. The rest held. Grading a known-answer test is mechanical. There is nothing to argue about.
Step 5. A human signs the verdict
A named person, never an AI grading itself, reviews the evidence and signs one of four honest answers: pass, pass with a fix, fail, or not enough evidence to say. You get the receipt: what was tested, what happened, what was not tested, which version of your workflow it applies to, and the signature. It's stamped with a digital fingerprint (a hash) so nobody, including us, can quietly edit the results later.
“Does it check my old work too?”
The core service crash-tests the machine as it is today, catching the next mistake before a customer does. There's an optional look-back: sample last month's real outputs and reconcile them against what they should have been. That is usually where the ugly surprises turn up. Ask about it in your fit check.
What this costs you in effort
- One short written fit check (free) to confirm your workflow qualifies.
- About 30–60 minutes of your team's time to run the drill.
- That's it. We do the rest.