Productizing Expert Networks for Scale
Joe Kim, CEO of Office Hours, on the end of crowdwork
This claim points to the core reason expert networks have historically scaled like law firms, not software. In the traditional model, growth means hiring more client service staff to find experts, persuade them to respond, screen them, schedule calls, and manage compliance by hand. That raises cost, slows turnaround, and makes service quality depend on the judgment of individual employees instead of a repeatable product workflow.
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Tegus described the same bottleneck in concrete terms. It had around 100 operations staff arranging calls, and analysts could spend half their time just finding experts and chasing replies. That is the signature problem of a people heavy services business, each new call needs human labor to move it forward.
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That services structure also shapes who gets prioritized. In older networks, the paying client sits at the center, so teams spend time on customer hand holding, while expert relationship building is treated as secondary. The result is lower expert retention and constant backfilling, which compounds operating load.
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Office Hours is organized around replacing those manual steps with product. The company frames the job as search, match, trust, scheduling, and incentives, then builds software for both sides of the marketplace. That opens the market beyond big hedge funds and consultants to product teams, founders, and user research buyers who cannot support white glove economics.
The next phase of the market is a shift from brokered calls toward productized access to human insight. The winners will be the networks that turn sourcing, matching, interviewing, and reuse into software, because that is what lowers cost, speeds response time, and makes expert supply durable enough to support larger markets like user research and AI training.