Who Does the Review
A promise of human review needs a credible answer about staffing, time, and what the reviewer can actually stop.
When a founder says every important output is reviewed, I want to know who is doing it and how much of their day it takes. That answer could change the buying decision.
Put numbers on it with a hypothetical process that receives 100 cases an hour. Ten percent go to a reviewer, who spends eight minutes on each. That creates 80 minutes of review every hour. One person cannot keep up, even before breaks or other work.
Allow that person 48 minutes an hour for review, leaving some room for uneven demand, and the allocation supports 60 incoming cases at the same referral rate. The proposal is still for 100. The plan needs to account for the remaining 40.
I would not approve 100 cases an hour on one reviewer. Adding a second qualified reviewer on the same allocation brings capacity to 120 cases an hour; until one is in place, intake should stay at 60. Lowering the review rate to make the numbers fit is a poor solution if those cases still need inspection. Work above the limit needs somewhere to go.
The same arithmetic belongs on the product roadmap. Showing the source beside the proposed answer, or making a correction quicker, could save more of the reviewer’s time than generating the draft a little faster. Cutting review from eight minutes to six would let one reviewer’s allocation cover 80 cases an hour. A founder should measure a change like that before promising it.
Reviewers need authority to act on what they find. They should be able to reject an action, correct the result, or escalate it to a named owner. When the assigned person is away, the work needs an agreed route or has to wait.
The appropriate review depends on the action. An internal draft based on public information might be checked by sampling. A payment instruction calls for a stronger intervention before execution. An approval screen after the payment has gone out cannot serve the same purpose.
I’d ask for actual review activity over a representative period, including the busiest days. That should show whether the proposed team can handle the volume and which product changes would help most. It also shows what reviewers change. If reviewers approve nearly everything, I’d inspect a sample of those decisions. The outputs may be sound. What matters is whether reviewers can identify and stop the errors the control is meant to catch. The customer is committing people’s time as well as paying the subscription.
Further reading: NIST’s Generative AI Profile, MAP 3.4, on operator proficiency and human review.