We gave Claude the same data

To test our own pitch, we gave a fresh Claude session the CRM export behind our sample plan and the brief a VP of Sales would write: the target, quotas, planned hires and their assumptions. It took about six minutes. Here is what it got right, and where its plan breaks.

Corvid Analytics is a fictional company with synthetic data. Test run October 18, 2026. Same files, same facts, no help from us.

What it got right

More than we expected. It didn't take the team's assumptions at face value:

It tested the 4-month ramp and 80% attainment against history and cut the plan from $8.05M to $6.91M. Our engine went from $8.27M to $6.54M.
It found that Enterprise runs on one AE, measured attrition at about 10% a year, and removed the same 16 duplicate deals.
It flagged that about a quarter of deal value is usage commit, so Finance has to confirm what counts as bookings.
It said pipeline creation has to grow by about a third, and set a $7.5M commit. So did we.

If you have a RevOps lead with a free afternoon, an AI tool gets you most of the diagnosis. The diagnosis isn't what breaks.

Where it broke: the decision

It hired for capacity the pipeline can't feed. Its own math said pipeline creation must grow 34%. Its recommendation, 6 new AEs, assumed that growth happens. Our plan limits bookings to the pipeline the company can actually create, so each added AE has deals to work.
It assumed hiring at a speed most teams can't. Four AEs starting in January means offers accepted in about six weeks, alongside managers and recruiting. Our plan schedules every hire on a realistic monthly calendar.
It gave one answer. No alternatives, and no way to compare them. Our plan puts every option on the same measure, the cost of each dollar it books, and shows why the recommendation wins.

Side by side

	AI tool, one prompt	Gapline
Their assumptions, then the data	$8.05M, then $6.91M	$8.27M, then $6.54M
Recommendation	Hire 6 AEs, 4 starting in January	3 AEs and 3 SDRs on a hiring calendar, plus outbound and partner fixes
Is the plan feasible?	Pipeline gap noted, not built into the plan	Bookings capped at the pipeline the company can create
Hiring pace	All starts treated as possible	No month starts more people than the team can hire
Options compared	One answer	Every option scored on bookings, cost, and cost per $1 booked
What you get	A spreadsheet and a written answer	Plan page, Excel model that ties out to the dollar, summary deck, 90-day cadence, KPIs
Who stands behind it	No one	An operator who has run this for a board, and can run it with you

The AI result came from one well-written prompt that included every assumption. A shorter prompt would likely do worse; we didn't test against that.

What you're paying for

The model is the easy part. You're paying for a plan that can actually happen, a clear decision between the options, and someone accountable for running it.

See how the sample decided Get your free gap report