Tim

SERV is proud to be among the founding partners of IC, collaborating with leading protocols like NEAR, MetaMask, OKX, Nansen, BNB Chain. Bringing a shared trust layer to the agent economy, projected to drive $5 trillion in commerce by 2030. Entire agentic stack in one flow 👇.

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!

Q2 was the quarter SERV went from thesis to proof. Q3 is where it starts becoming real infrastructure that companies depend on. What happened in Q2 in a nutshell: > Private beta went live, bringing SERV Reasoning into real production across network intelligence, robotics, AI verification, and more. > Greg Ivanov, ex-Google Head of Partnerships, joined as advisor to open enterprise doors and scale our operations globally. > Neol, using SERV Reasoning hit 100% reliability in production with the UAE government, the highest trust bar in software, cleared. > SERV-armed models beat Anthropic's flagship Fable at a fraction of the cost - proof that small models enhanced with SERV can top frontier ones. > Every major model and stack integrated and made enterprise-ready fast: Gemini, Claude, Gemma, GLM, NVIDIA Nemotron, Fusion. But what's going to come in Q3 is even bigger. We're taking SERV into the markets and industries that need it most. What's coming in Q3: > Major long-term partnership coming in July - one of the most significant crypto deals any web3 company has ever signed. > Global banking, financial and neobanking industry expansion across the US, Europe, Singapore, and Africa, backed by the certifications and legal entities each market requires. > Robotics industry active SERV pilots moving toward completion. > SERV Reasoning V2 - our biggest upgrade yet, built for the most demanding clients and enterprises. Including: Multipath Reasoning, which lets SERV handle huge, contradictory rulebooks. Shadow Agents that check every decision. And with new benchmarking tooling, any company can see exactly what they'd save before switching - all while their data stays sealed behind the Privacy Stack. > Community-centric initiatives to propel our message in new channels. > Attending multiple major AI and finance events, talking and closing deals with big companies that get us closer to the mass adoption. Q2 proved the technology works. Q3 is where SERV becomes the reasoning layer enterprises and governments build on.

Q2 was the quarter SERV went from thesis to proof. Q3 is where it starts becoming real infrastructure that companies depend on. What happened in Q2 in a nutshell: > Private beta went live, bringing SERV Reasoning into real production across network intelligence, robotics, AI verification, and more. > Greg Ivanov, ex-Google Head of Partnerships, joined as advisor to open enterprise doors and scale our operations globally. > Neol, using SERV Reasoning hit 100% reliability in production with the UAE government, the highest trust bar in software, cleared. > SERV-armed models beat Anthropic's flagship Fable at a fraction of the cost - proof that small models enhanced with SERV can top frontier ones. > Every major model and stack integrated and made enterprise-ready fast: Gemini, Claude, Gemma, GLM, NVIDIA Nemotron, Fusion. But what's going to come in Q3 is even bigger. We're taking SERV into the markets and industries that need it most. What's coming in Q3: > Major long-term partnership coming in July - one of the most significant crypto deals any web3 company has ever signed. > Global banking, financial and neobanking industry expansion across the US, Europe, Singapore, and Africa, backed by the certifications and legal entities each market requires. > Robotics industry active SERV pilots moving toward completion. > SERV Reasoning V2 - our biggest upgrade yet, built for the most demanding clients and enterprises. Including: Multipath Reasoning, which lets SERV handle huge, contradictory rulebooks. Shadow Agents that check every decision. And with new benchmarking tooling, any company can see exactly what they'd save before switching - all while their data stays sealed behind the Privacy Stack. > Community-centric initiatives to propel our message in new channels. > Attending multiple major AI and finance events, talking and closing deals with big companies that get us closer to the mass adoption. Q2 proved the technology works. Q3 is where SERV becomes the reasoning layer enterprises and governments build on.

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!

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!

Some good best practices here on AI token cost optimization. None of these happens though without a deep understanding of the underlying work being done in a non-abstract way. The ultimate implication is that a layer between the work itself and the underlying intelligence needs to deeply understand your workflows, context, and business process. Now, each individual company doing this on their own is unlikely to be effective at scale, so as a consequence, this is effectively the playbook for any applied AI company right now. By evaling the models for the applied use cases, deeply understanding the domain, having tuned UX and features for the use case, and having the ability to support adoption and change (via FDEs), allow this layer to add a ton of value. And as a result, enterprises get higher ROI because you actually can get *more* intelligence per dollar by having optimal architecture and workflows. There will be many horizontal and vertical versions of this approach. Huge opportunity right now.

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching. Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work. Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task. Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented. Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted. Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect. The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable. Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.