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Check a warning against a known answer before a manager sees it
Pipeline warnings that nobody has checked are just noise with confidence. So before launch we built a test account where we knew every right answer, and the product had to match all 23.
An AI sales product we built watches a company’s CRM and warns managers about risky deals: overdue close dates, large deals that have gone quiet. A warning a manager acts on had better be right. The problem: those rules had never been seen to fire on realistic data, and the only way to know a warning is right is to know the answer in advance.
Plant the answers first
Waiting for real data would have meant shipping rules nobody had seen fire. Instead we built a test account with 24 sample deals, plus contacts and companies, where we planted every problem on purpose and wrote down which warnings should appear.
Then we synced it end to end, the same way a customer’s CRM would be synced, and compared the product’s warnings with the planted answers.
Rules tested
16
Pipeline warning rules across 3 agents
Correct
23 of 23
Warnings matched the planted answers
Fixed
15
Defects found and fixed before launch
What the known answers caught
The run found and fixed 15 defects along the way, the kind that only show when real-looking data flows through every step. It also confirmed the behaviour a manager actually wants: a deal leaves the warning list the day it gets a call.
What it proves
The rules do exactly what they should on a known answer, before any customer relies on them. The same test stays in place for every change, so real deals always meet rules that have already been proven.
How we read data
We check a warning against a known answer before a manager sees it.
The examples in this article come from real engagements. Client details withheld; every figure comes from the client’s own data.
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