Most onchain APIs return data. Agents need decisions. HYRE just upgraded 7 decision endpoints to SERV Reasoning by @openservai a reasoning engine that thinks before it answers. → Token Verdict: snipe / watch / avoid → Bridge Quote: execute / wait / avoid → Yield Migrate: migrate / stay / wait (with break-even math) → LP Recommend, LP Strategy, LP Rebalance → /ASK: natural language, reasoned answers Fast endpoints stay fast (~1s). Decision endpoints now reason (~4-6s) — because a wrong financial signal costs more than 3 extra seconds. Don't take our word for it every response includes a model_used field. Check who answered.

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).

Just came back from Kenya after a week of conversations on AI, finance, and institutional adoption. By the end of the trip, we had met with the largest investment bank in the country, half of their top 10 banks, fintechs, former ministers, and senior leaders with experience across global institutions. A few takeaways stood out. 1. AI adoption is now a boardroom mandate. 100% of the institutions we spoke with had a direct mandate to explore AI implementation. The reason is simple: the productivity gains are too large to ignore, and no major institution wants to be the one that lets competitors move first. 2. Deployment is still early. Despite the intent, only around 50% of organisations we spoke with had any live AI deployment. Of those, more than 80% were still using basic general-purpose model implementations. That creates a major gap between interest and real adoption. Institutions understand the potential, but most are still in the early stages of figuring out how to move from experimentation to workflows that can operate across the organisation. 3. Auditability and transparency are non-negotiable. Over 90% of institutions we met had serious concerns around data security, and roughly 70% had already rejected third-party AI tools because of those concerns. For financial institutions, “AI-powered” is not enough. They need to know where data goes, how outputs are generated, how decisions can be audited, and whether the system can be trusted in high-stakes environments. 4. Reliability and cost are the main blockers to scale. Many institutions have already experimented with AI. The issue is that early pilots often failed to meet the standard required for broader deployment. Unrestricted access, context bloat, inefficient prompting, and unpredictable outputs made teams cautious on both cost and reliability. In banking, a tool cannot simply work in a demo or perform well under controlled conditions. It has to work consistently, transparently, and at a price that makes sense across the organisation. 5. The biggest barrier is not always technical. It is institutional risk. The larger the institution, the less incentive there is for any individual to take unnecessary risk. Maintaining the status quo is safe. Championing a new system is not. If it works, the institution benefits. If it fails, the person who pushed for it may carry the blame. That means serious AI adoption requires more than product. It requires trust, relationships, internal alignment, and a clear path from pilot to deployment. This is especially true in emerging markets, where enterprise sales cycles are long, distribution is relationship-driven, and adoption often depends on being in the right rooms with the right stakeholders. The opportunity is clear: major institutions are actively looking at AI, but most still lack systems that meet the requirements for real deployment. Secure. Reliable. Auditable. Economically viable. That is the bar. That is what we are focused on with SERV Reasoning. We will continue strengthening relationships across the region and using East Africa as a launch point into broader conversations across the continent. We are also continuing our work in the UAE through Neol, with government-side interest in expanding initiatives further. Next up: LATAM, South Asia, and other high-growth markets underserved by the major players in AI infrastructure. SERV worldwide.

Even at Google I’ve not seen this level of positive feedback on any product within a month of beta. Mind-boggling… 🤯

MESH is the privacy-first AI router: one OpenAI-compatible endpoint in front of 46 models, a signed receipt on every call, and zero payload retention. SERV is the reasoning layer adding structured, schema-forced, auditable reasoning that routes each step to the right model instead of burning frontier tokens on everything. Soon you'll be able to switch SERV on as an optional layer over every model and agent already on MESH What that unlocks: > Reasoning that stays inside defined bounds instead of drifting > Signed receipts for the privacy, structured auditable steps for the logic > Frontier-grade results without frontier-grade token bills MESH's verifiable privacy meets .@openservai's SERV reasoning. Coming soon.

at google play we looked for teams that had an "unfair advantage"; at least one of the following: - a tech moat (proprietary IP, novel architecture) - a structural moat (switching costs, lock-in) - execution speed and quality (a moat in itself, especially in commoditised verticals) rarest of unicorns are the ones that can combine all three. with @openservai we're seeing how they all combine -> genuine tech differentiation in reasoning architecture. structural lock-in once enterprises integrate. a team executing faster and cleaner than anyone else in the category.

We’re excited to be integrating SERV Reasoning by @openservai to bring our no-code robotics solution to market, and had the pleasure of joining their closed beta. Advanced robotics used to require a full team of specialists. Now you can build real behavior in our no-code studio — this Unitree G1 hand grasp function was entirely written by the serv-standard model inside ROBA's no-code studio. This is just a small glimpse of what can be done in our platform. In our benchmarks across real builder tasks, SERV's models matched the quality bar we needed while cutting AI costs by over 80%. SERV Reasoning will now be the default inference engine for the upcoming release of our ROBA no-code studio on June 1st.

Huge appreciation to the @openservai team for their full support throughout the process. After 4 years of working with AI models, I can honestly say SERV Reasoning is one of the best combinations of performance and cost efficiency I’ve used. Better reasoning. Faster responses. Lower cost. A significant step forward for XONA.

Our agent just got a major upgrade - XONA agent is now powered by SERV Reasoning by @openservai. We have been testing SERV in the Private Beta and it has far outpaced our default production stack running on Gemini frontier model. SERV delivers faster responses at a significantly lower cost, while maintaining the same 100% reliability in our workflow benchmark. SERV Reasoning vs. Gemini on our agent workflow: → Accuracy & reliability: 100% for both models (20/20) → Latency: SERV responded 1,004 ms faster → Cost efficiency: SERV is 5x more cost-efficient This upgrade helps us deliver better-performing resources for agents across the Agentic Commerce ecosystem. Same reliability. Faster execution. Lower cost

🚨 THE AI COST CRISIS HAS STARTED. Microsoft reportedly told engineers to stop using Claude because AI bills were exploding, while Uber says its entire yearly AI budget was already destroyed by April.