Dan Haberern

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Dan Haberern

Dan Haberern

@ServReasoning

Business development at @openservai. Helping enterprises run AI agents that are cheaper, faster, and actually reliable.

انضم Mart 2026
2 يتبع169 المتابعون
Dan Haberern
Dan Haberern@ServReasoning·
One thing you learn pretty quickly in BD: You can't pitch every company the same way. Even if the product is the same, the pain is not. A startup hears "SERV Reasoning" and usually cares about speed, cost, and whether they can plug it in without slowing the team down. A large enterprise cares about control. A bank or compliance-heavy org cares about auditability, hallucinations, decisioning, privacy, and whether the system can actually survive internal review. Same product. Different conversation. That's been the interesting part of the SERV Reasoning meetings lately. The value is very clear once people understand it: - better reasoning accuracy - near-zero hallucinations - lower agent costs - structured decision routes - shadow verification - single-line integration But the way you frame it depends entirely on who is sitting across from you. In my experience, good BD is mostly pattern recognition. Who actually owns the pain? What do they get measured on? What happens if this problem doesn't get solved? What would make them trust the solution enough to move? Once you understand that, the conversation gets a lot easier. Especially when the pain is already obvious.
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Dan Haberern
Dan Haberern@ServReasoning·
I run business development at @openservai, onboarding enterprises onto SERV Reasoning. Over the last few weeks I've been in deep conversations with CTOs, Heads of AI, and engineering leads across F500s, fintech and biotech startups, and some of the most innovative companies building right now. The reception has been unlike anything I expected. "This is exactly what we've been waiting for" came up more than once. Meeting with one of the top 50 startups in San Francisco this week. Hundreds of millions on the line and they can't afford to trust AI they're not sure about. But the pattern is the same everywhere, startups or Fortune 500s: teams are running AI agents at scale, costs are out of control, reliability is broken, and nobody has solved both at once. We have.
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