Tegus solving expert sourcing bottleneck

This is the core bottleneck that separates expert networks from software businesses. Tegus was not struggling to host transcripts or search pages, it was spending heavy labor on the messy first mile of matching one investor question to one real person, usually by searching LinkedIn, finding contact details, and starting outreach. That is why speed of sourcing mattered as much as price, and why Tegus built internal tooling around discovery, contact retrieval, and outbound workflows rather than just the transcript product.

• Traditional players like GLG and GuidePoint had a structural advantage because they already had massive expert rosters they could tap quickly. Tegus could charge less for calls, but it still had to win the race from client request to booked call, which made sourcing efficiency a daily operating priority.
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• Tegus treated the call as content creation, not just matchmaking. Analysts did the hard work to find and book experts, then the resulting transcript fed a subscription library priced around $25K per seat, while call fees were closer to pass through costs of roughly $300 to $400.
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• This friction also explains why newer marketplace models focus on making the supply side more self serve. Office Hours describes the same pain point as a search, match, discovery, connection, and incentivization problem, with product design aimed at reducing the manual work humans traditionally did in the middle.
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The direction of travel is clear. The winners in expert networks will turn analyst labor into software and reusable data, while keeping humans focused on the few steps that still require trust and persuasion. As AI handles more search, tagging, summaries, and scheduling, the scarce advantage shifts toward proprietary expert supply, faster conversion, and a larger library of interviews that compounds with every completed call.