Dan Haberern @ServReasoning

I spent the entire last week at the AI Engineer World's Fair in SF with where top AI labs, founders, Fortune 500 CTOs & AI Engineers meet. Really perfect timing - having boots on the ground right before we deploy SERV Reasoning v2, because the problems v2 ships against are exactly what i heard in meetings, over and over. To give you a quick recap, it was a fruitful week overall: 60+ new companies from the fair now in our structured pipeline, from two-person agent teams to trillion-dollar clouds (a few that you'd recognize instantly, and at least two are infra your own stack probably touched today). One of the most interesting part was the Startup Battlefield where new startups pitched their projects. After numerous meetings, one thing is clear: everyone in Enterprise AI is doing it backwards. The current flow: 1.) Tune the model 2.) Ship the agent 3.) Debug a black box after it embarrasses you in production A version of the same confession kept surfacing: "we shipped an agent, it did something weird in front of a customer, so we pulled it - cause nobody on the team could explain a single decision it made." Others told me they burn anywhere between $10-$90k (!) a month on inference and can't drive it down. It became "cost of doing business." Now that SERV v2 is here, we are solving both these issues. Two confessions with two direct answers in v2: 1.) The black box: SERV makes agent reasoning traceable - you see how the agent thinks, not just what it outputs. And with Shadow Agents, every output gets reviewed against the original brief by a separate verification agent before anything ships. The "weird decision" gets caught in verification. Trust first, then scale. 2.) The burn rate: the reasoning engine lets you run the same workloads on much smaller models with better outputs. Verification Hints give agents signal on what a correct output looks like before they generate, cutting expensive re-work. And you don't have to take our word for any of it - Benchmark Tooling shipped in v2 shows you the cost savings on your own workloads before you integrate. That's the whole idea behind SERV Reasoning v2. Judging by last week, it's exactly what the room is starving for. Q3 is starting off with a bang.

GM from San Fransisco! Currently got boots on the ground at the AI Engineer World’s Fair, with a stacked attendee list of >5000 high signal senior technical personnel from top AI companies. Got pre-arranged meetings with 4 companies lined up today; robotics, aerospace, a couple others, all of them trying to run agents in controlled, predictable settings. That's the whole idea behind v2 of SERV Reasoning, so these are turning into real conversations and momentum heading into Q3. One thing that's stuck with me: the teams deepest into production are the ones obsessing over reliability, not capability. Excited to be in the room this week, more to come!

At the AI Engineer World's Fair this week, and I came with a full calendar, not just a badge. The conversations hitting different this year are the ones about production. Teams running agents where the inference bill is starting to hurt and "works in the demo" stopped cutting it. So much of this space has shifted that way, fast, and it's exactly the I'm here to solve. Biggest thing I've picked up doing BD at these: presence isn't being everywhere, it's being in the right few rooms. Book your meetings before you land, skip the booth circuit, follow up the same night while it's fresh. If you're building agents that actually have to work in production, come find me. Would love to trade notes. Around SF all week!

SERV is building the reasoning layer the frontier labs can't ship. Their revenue depends on heavy inference spend. SERV does the opposite: it makes smaller models - including open-source - reason just as well on the same workloads. A fraction of the cost, with one line of code.

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.

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.