A funnel that recruited and vetted
We built an onboarding pipeline that sourced transcribers, tested them against real audio, and only let through the ones who met Verbit's bar. Nobody self-selected onto the work.
Case study
Verbit.ai: a dedicated team instead of a crowd.
Verbit.ai uses AI to disrupt transcription and captioning. Their growth depended on the quality of crowdsourced contributors, and the crowd was the constraint.
The problem with the crowd
Verbit's capacity to grow was tied directly to the quality of the work its crowdsourced contributors produced. In practice, crowdsourcing individuals pushed their operational costs up rather than down.
The deeper problem was cherry picking. Contributors took the easy files and left the hard ones, so not all work got completed, and the work that did get completed was the least representative of what Verbit actually needed. Scaling the company on that foundation was not possible.
What we did
A QA system designed with the client
We built the quality process together with Verbit rather than reporting on our own marking. That gave the team a standard to hit and Verbit visibility into whether it was being hit.
A private network for upskilling
We ran a dedicated community where transcribers learned from each other and from us. People got better at the work over time instead of churning out at the same level they arrived.
What changed
Within two months Verbit had 250 trained transcribers working only on their queue, and the team kept growing from there. Because the same people stayed on the work and kept improving, quality held as volume rose, and the operational cost of each minute came down rather than up.
The relationship widened past transcription: Verbit handed us their customer support work as well. That is usually the clearest signal that a managed team is working, as clients only give you the next thing when the first thing is running without them.
If your queue depends on a crowd, this is the pattern
Cherry picking, rising cost per unit and quality that drifts as volume grows are not problems you fix with more contributors. They are what happens when nobody is accountable for the whole queue. A managed team, calibrated against your bar before it scales, fixes all three at once.
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