SERV Reasoning Private Beta is one month in. Don't take our word - hear from builders inside. 👇

The US government pulled Claude Fable today. Good news is, you don’t need frontier models for production-grade agents. With SERV, open-source LLMs like DeepSeek-v4 outperform Claude Fable at up to 90x cost savings. The decentralised future is bright. SERV is inevitable.

Welcome on board. All of HYRE’s decision endpoints are now powered by SERV Reasoning.

NVIDIA shipped a new frontier model - Nemotron 3 Ultra, and we have already integrated it with the SERV Reasoning engine. The base model scored 79.61 on our standard DeFi benchmark. Armed with SERV, it jumped to 90.78. Thats 11.17 points gain. SERV makes all models smarter.

SERV Reasoning cut verifier agent costs by 99%, making continuous AI checks economically viable inside Sentinel. It’s the trust layer for autonomous AI: the gate between “agent decided” and “transaction sent" - finally deployable at scale. Every Sentinel call runs on SERV.

Several SERV Reasoning-armed agents just beat Anthropic's Fable, one of the strongest LLMs ever built, at up to 90x lower cost. ​ That result comes from using SERV Reasoning with DeepSeek-v4-Flash on our DeFi benchmark. Thanks to the SERV engine, agents running on smaller models perform better than those using frontier, expensive ones. ​ Here is more information about the benchmark behind that result, what it tests and why it is built the way it is. ​ Why a DeFi benchmark ​ Autonomous trading is one of the harshest tests of machine reasoning. ​ An agent reads live market state, portfolio state, and a strict risk policy, then has to commit to one of four actions: BUY, SELL, HOLD, or BLOCK. A wrong decision costs real money. ​ No room for reasoning sounds smart but lands on the wrong trade, which makes it the ideal domain for measuring whether a model actually follows rules under pressure rather than just explaining them well. ​ What the scenarios target ​ Each scenario combines a market snapshot, portfolio size, trading signal, and a fixed risk policy, and falls into one of three families: - clear constraint violations the agent must refuse - ambiguous setups where everything looks tradeable but the conditions say wait - valid trades where the agent must size the position correctly within caps ​ This mirrors how trading agents actually fail in production. Rarely on the obvious cases, almost always on the judgment calls. ​ How it is scored ​ The benchmark follows the same conventions as the agentic evals in the latest frontier model reports, including τ²-bench and Terminal-Bench: - outcome-verified scoring, where code checks the final decision against the risk policy, with no LLM judges - identical prompt, scenarios, and settings for every model - zero-shot, with no scaffolding, no retries, and no few-shot examples - repeated runs per scenario, so consistency is measured alongside accuracy - cost computed from real token usage at list prices, per run ​ Why this is exactly where reasoning matters ​ This task has the three properties structured reasoning is built for: hierarchical rules, multiple data sources that must be reconciled, and a verifiable correct answer. ​ SERV's bounded reasoning keeps a model moving through that hierarchy step by step, instead of letting it talk itself into a bad trade. ​ That is why SERV-routed models clear the same quality bar as flagship models at a fraction of the cost, and why the gap shows up most on the judgment calls.

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.

SERV Reasoning-armed models just beat Anthropic's newly released flagship Fable (Mythos) - one of the strongest LLMs ever built - at up to 90x lower cost. With SERV, enterprises can finally afford AI at scale. We spent the last two years building for exactly this moment. The labs promised costs would halve every 6 months - instead, prices keep climbing, subsidies are ending and the math breaks. There is a way out. SERV-enabled models vs Claude Fable 5 (85.17 ~3.24¢): → DeepSeek-V4-Flash: 87.15 - wins at 90x lower cost → NVIDIA Nemotron: 90.78 - wins 5 pts, 11x lower cost → Gemma 4 12B: 83.33 - within 2 pts, on local hardware Top-tier performance no longer requires the most expensive model. And cost is only half the story - production AI must be reliable, auditable, private, and secure, or it dies in procurement. SERV is built for all of it. The agentic economy finally has the infrastructure to run on.

Tim Hafner, our co-founder, is in London for the AI Summit. Over the last few months, we've seen enterprise deals move fastest when handled face-to-face. It's why we're present in boardrooms and major events across the globe, from the US to Europe and Africa. Stay tuned.

SERV is the agentic reasoning engine solving exactly this - for enterprises, financial institutions, banks and even governments deploying AI in production. The next frontier is reasoning compression. Cheaper models reduce the cost of intelligence, but they do not solve the cost of decision-making. Most business decisions are not about raw IQ. They are about applying the right process, policy, workflow, or rulebook reliably. That is where unbounded inference wastes money. SERV Reasoning makes reasoning bounded and machine-readable, so models spend fewer tokens getting to reliable decisions. Better performance per dollar, not just cheaper tokens. Already live with the UAE government and a fast-growing number of enterprises, financial institutions and startups across many industries in our private beta (inc. banking, compliance, security, health, robotics, among many others).

SERV Reasoning rollout is moving faster than anticipated. We have been swamped with interest from agent builders and enterprises hoping to embed the reasoning into their agents. Where we are right now: Phase 1 (done): Private beta rolling out to selected teams. Phase 2 (next up): Public API, self-serve onboarding. Phase 3: SERV-native fine-tuned models Phase 4: Purpose-built SERV model from scratch Phase 5: maLLM - morpheme-aware LLM Every team gives us feedback on how SERV Reasoning performs against real-world problems.

Companies running production agents in 2026 are not optimizing for which model is "the smartest." They care about security, reliability and cost efficiency at scale. SERV brings all three.