OpenServ

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.

SERV is entering the US financial system - the world's largest market, handling trillions of dollars in assets. We are targeting banks and fintech startups. The upcoming SERV v2 architecture brings us closer to meeting our partners' strict trust and reliability requirements.

OpenServ CTO @dashersw goes live today with fellow Internet Court founding partners @lifiprotocol and @Kleros_io. Join at 5PM UTC -> @GenLayer.

This shift supports the thesis behind SERV, and it is already happening. With SERV, smaller models (open-source or closed) beat frontier LLMs like Fable at up to 90x lower cost, as shown in our benchmarks. SERV is monetizing the shift in the AI market that's already unfolding.

SERV’s Vision and Plan for Banking In this post we're going to lay out our plans for the banking industry in 2026. Our goal is to bring SERV reasoning into the world’s largest global enterprise markets and establish it as the new industry standard. The strong interest we’ve already received from enterprise is a clear testament to the significant demand for this technology, with banking and finance emerging as one of the most active sectors of interest. The opportunity in this market for SERV is enormous: - Financial institutions are projected to spend $97 billion on AI by 2027, growing at a 29% CAGR, exceeding $200 billion annually by 2030. - McKinsey est. the annual value of AI in banking at $200–340 billion. - Citi projects that AI will add $170 billion in global banking profits by 2028, helping push sector profits toward $2 trillion. This is the trillion-dollar opportunity we are targeting. Why financial institutions are turning to SERV: In banking, where trillions of dollars are at stake, the top three roadblocks preventing widespread AI agent adoption are trust, reliability, and cost-effectiveness. This is exactly why leading financial institutions are turning to SERV. Our technology, built in close collaboration with our enterprise partners, allows financial institutions to accelerate their AI adoption while ensuring it happens in a secure, reliable, and commercially viable way, delivering breakthrough performance without compromising on risk, control, or regulatory compliance. ——- What a bank actually requires to implement agents Every automated system that touches a regulated decision passes through model risk management - SR 11-7 in the US, SS1/23 in the UK - and in most cases, a named bank executive is personally accountable for every AI-driven outcome. That machinery is what we are currently preparing our engine for, fitting all markets SERV is targeting including the US, UK / EUR, Africa, and Singapore. Just a brief look through current compliance ecosystem gives us a good understanding of where things are going: - last April, US regulators revised their model risk guidance (SR 26-2) and explicitly placed generative and agentic AI outside its formal scope. The assurance playbook for agents now has to be built, not inherited from official policies. - this month, the FCA published the Mills Review, laying the foundations for agentic finance in the UK: autonomous agents are coming to retail banking, the regulator will supervise them with its own AI, but accountability stays with named humans. - the EU AI Act classifies creditworthiness assessment as high-risk. After the Digital Omnibus, those obligations land on 2 December 2027 - a fixed deadline banks are being told to spend on governance work. - Singapore's MAS is finalizing AI Risk Management Guidelines that explicitly cover AI agents: full AI inventories, risk materiality assessments, lifecycle controls - and no shifting of accountability to vendors. Four jurisdictions, with one convergent demand: reliability, explainability, auditability, bounded autonomy. None of it can be satisfied by a smarter model. These are precise architecture requirements that our system is ready to meet. The industry's own numbers describe the gap precisely. 99% of companies plan to put agents into production; 11% have (KPMG). 57% of banking executives expect agents fully embedded in risk, compliance, audit and fraud functions within three years (Accenture). ——- How SERV clears the bar Validation: SERV Reasoning Graph Sharding decomposes every agent decision into bounded, schema-forced steps - deterministic code where it can be, model calls only where it must be. A model-risk team reviews a reasoning graph, not a prompt, and the same input produces the same reasoning trace - the property that validation, revalidation, and challenger testing actually require. Auditability: SERV Audit allows insight into decision chains as first-class artifacts: inputs, rules applied, intermediate conclusions, confidence, approvals - exportable as evidence packs shaped for model-risk validation files and AI governance review, and explainable to a supervisor or a customer. Reliability: Shadow Agents and Verification Hints validate every draft at runtime against the brief, policy and constraints before anything ships, and gate irreversible actions behind explicit checks and human checkpoints. Multipath Reasoning lets contradictory rulebooks - credit policy, risk appetite, regulatory constraint - coexist in one reasoning graph, because that is what real banking decisions look like. Security: PromptGuard screens every request inbound for injection before the model runs, and every output outbound for leakage before release. Prompts and data are never stored or trained on, and private, encrypted inference (TEE plus end-to-end encryption) is built for regulated data and residency requirements. Economics: Verification only matters if it pays for itself. Bounded Reasoning Graphs are authored once and amortized across millions of runs, so smaller models execute them reliably - frontier-grade results without frontier-grade unit costs. ——- SERV Roadmap for the Banking Industry H2 2026: - First PoCs / pilots signed with banks and financial institutions - Banking benchmark program opens - Legal entities live across US, Europe, Singapore, Africa - Banking-grade hires: model risk (SR 11-7/SS1/23) - Certifications secured (SOC 2 / ISO 27001) - Neobank integrations - Major DeFi protocol integrations - Fintech pilots converting to paid production 2027: - First Tier 1 bank in production - Agents touching payments under SERV verification - Agentic commerce verification layer ——- Why the window is now Agent-executed payments went live across 30+ card issuers just this month. Regulators on three continents have published their assurance bars. And yet, up to 40% of enterprise agent projects are still expected to be cancelled by 2027 on cost and risk controls. The institutions that win the agent era will be the ones whose agents can be validated, audited, and trusted with real money. One bank becomes the reference, the next ten follow. SERV layer is already running in government production. Banking is next.

As part of our V2 launch campaign, we'll be holding an AMA in our t.me/openservai Telegram community on Thursday July 16th, at 6PM CET. Join the TG & drop your questions now, to be answered during the session.

@NEARProtocol see you in court x.com/openservai/sta…

Internet Court is live: the open skill where any two agents can structure a deal, hold funds in escrow, and settle disputes in natural language. NEAR gives agents wallet + transaction skills, cross-chain swaps via NEAR Intents, and verifiable private inference on NEAR AI Cloud.

@MetaMaskDev see you in court x.com/openservai/sta…

Proud to be part of Internet Court, the open skill where agents pay, escrow, and every deal sets up front how it will be settled. MetaMask provides the wallets and ERC-7710 delegation that define what an agent can do with funds. 🦊

In the next deep dive on our upcoming v2 launch: Multipath. A new architecture born from hands-on work with customers - built with corporate rulebooks in mind, with conflicting rules and information written by different teams. v2 changes how SERV Reasoning works - more soon.

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 👇.

OpenAI's new GPT-5.6 $1 model (Luna) just beat the $5 flagship (Sol) on our agentic benchmark. With SERV Reasoning, all three models performed better, while Luna outscored every configuration tested. Thanks to SERV, failure rates dropped by up to 42.7%.* That's the thesis SERV is built on: Reliability in AI is the real product. Large-scale AI adoption is waiting on the layer that makes AI dependable enough for production. Standout findings: - Luna + SERV tops the table at 1/5 of Sol's price. - These tiers behave less like mini/nano distillations and more like independent takes on one architecture with distinct post-training. - Luna behaves like a smaller model RL-trained hard for agentic instruction-following and steerability, which would explain why it takes the biggest lift from SERV. *i.e. the relative drop in failure rate: Luna went from 12.89% failed tasks to 7.39%. More results and insights coming soon.

Moments like this make SERV Reasoning privacy solutions ever-more relevant for enterprises. We do not retain prompts - so there is nothing to scan. Running agents via SERV API will offer max protection for our users, making them sovereign and independent of regional policies.

Our vision is to become the #1 player in every market we enter. We just announced Q3 plans: expansion into global banking and fintech, alongside humanoid robotics - and the release of SERV v2 in mid-July. Everything shipping in v2 is a direct answer to what banking demands before it trusts AI agents with real workloads. TLDR: The financial services AI agent investment cycle is starting right now, with hundreds of billions in spend coming. First movers among the banks stand to capture billions in extra profit while the latecomers get stuck with an uncompetitive cost structure. Adoption is being held back by two things - trust in agents and the cost of running them at scale. Those are the exact problems SERV was built to solve, and v2 is the biggest step we have taken toward solving them. Some key metrics and trends we are looking at: - Financial institutions are on track to spend $97B on AI by 2027, growing at a 29% CAGR - implying $200 B+ annually by 2030. - 83% of financial services professionals plan to increase AI spending. - McKinsey puts the value of AI in banking at $200-340 B per year. - Citi projects AI lifts global banking profits by $170B - 9% - by 2028, pushing sector profits toward $2 trillion. - BCG's 2026 estimate: AI can raise bank profitability by 30% and cut costs 30-40% by 2030. This is the trillion-dollar market we keep pointing at: AI adds $13T to global GDP by 2030, and banking's slice alone surpasses $150 B. Why the urgency by banks? Because the first-mover math is brutal. Banks that lead on AI gain a projected 4-point ROTE advantage over laggards. For a T1 bank, 4 points of ROTE is billions in extra profit, every year. Early adopters see 2.84x ROI on AI investment versus 0.84x for the laggards. In our research about 70% of banking executives believe AI will directly drive revenue growth while 32-39% of work inside financial institutions has high potential for full agent automation, another 34-37% for augmentation - up to 76% of all work, with cost reductions of up to 70% in some categories. The trend is clear. In 2025 alone, 50 of the world's largest banks announced over 160 agentic AI use cases. AI is being adopted faster than PCs, mobile phones, and the internet itself. The cycle has started. And yet almost nobody has actually deployed: 90% of enterprises want AI agents in production but only 11% have them there, with most deployments killed by unclear ROI and weak risk controls. A third of reported negative consequences from AI adoption come down to one word: unreliability. It is the most aggressive adoption curve of any emerging technology - and the largest gap between ambition and execution. Look closely at that gap and you will see why SERV v2 feature list is constructed the way it is: - Unclear ROI: Benchmark Tooling lets a bank measure cost savings and reliability gains on its own workloads before integrating anything. - Reliability: Shadow Agents verify every decision before it ships, pushing agent fleets toward 100% reliability - the only acceptable number where there is zero margin for error. - Contradictory rulebooks (in banking, this is called compliance): Multipath Reasoning lets agents reason through complex, conflicting rules in a single graph. - Cost at scale: Verification Hints and bounded reasoning cut re-work and tokens, so agents reach reliable decisions cheaper. - Security: Prompt Guard protects agents handling money from injection attacks, by default. With a lot happening behind the scenes, some of the work is already public. Our leadership already sitting in boardrooms with Tier 1 banks holding billions in collective assets. OpenServ on its way to securing certifications regulated markets require - unlocking pilots across banking and fintech, a $460B market in 2026. Neol running SERV Reasoning at 100% reliability in production with the UAE government. ThoughtProof - agent verification layer for banking, compliance, and onchain settlement - hitting 107x performance per dollar on SERV with zero failed calls. We believe this is the biggest economic shift since the steam engine, and financial services is where it lands first and hardest. SERV v2 exists so that when banking moves into AI - and it is moving now - it runs on OpenServ. The future is bright.

SERV is targeting a trillion-dollar market: AI is set to add $13T to global GDP by 2030. Banking's slice alone: $150B. Yet 40% of agent deployments are bottlenecked by cost and risk failures. That's exactly what SERV v2 tackles - launching mid-July.

Q3 is off to a strong start, with 60+ new companies in the pipeline. From 2-person agent teams to trillion-dollar cloud providers, every meeting surfaces the same two bottlenecks to AI adoption: black-box agents and runaway inference costs. SERV v2 is built for exactly this.

SERV Reasoning v2.0 and the Market Opportunity Mid-July, SERV Reasoning is upgrading to SERV v2 - the most important upgrade we've ever made to the SERV Reasoning engine. In high-stakes industries like banking, robotics, and governments, SERV is bringing trust, reliability, and cost efficiency. SERV v2 was designed and built with these factors in mind through collaboration with our users and partners in enterprise. The size of the market opening in front of us is staggering. This is a trillion dollar market by the 2030's. In banking alone the market is expected to surpass $150B in the coming years, and across general enterprise, adding over $13T to global GDP. Our goal at SERV is to put our technology into the core of that market through bringing to the table what enterprise and businesses need. It's estimated that up to 40% of all enterprise AI agent projects will be cancelled by 2027 through escalating costs and inadequate risk controls - exactly the issues SERV was designed to deal with. The core elements shipping in v2: - Multipath Reasoning: This is a foundational upgrade that enhances the core of the SERV Reasoning engine. In the real world, decision making is messy, complicated, and sometimes contradictory. Multipath Reasoning allows enterprise AI agents to make decisions with complex & contradicting rules, upgrading agent reasoning to better handle real life situations. - Shadow Agents: 100% Reliability is the baseline for high-stakes environments. SERV’s goal is to elevate agents to that level, and Shadow Agents bring us closer to our goal. Shadow Agents are separate agents who verify and review every decision before it’s finalized. In fleets of deployed enterprise AI agents, one bad decision by an agent has negative downstream impacts, and Shadow Agents work to prevent that from happening. - Verification Hints: Verification hints are extra signals given to the agents before output about what a correct output should look like. Verification Hints reduce re-work, cut costs, and increase the accuracy of outputs towards our goal of 100% Reliability. - Benchmark Tooling: A tool for potential enterprise customers to see the cost savings and reliability improvements of switching to SERV on their own work before integration. It supports existing enterprise partners engineering teams to optimize existing prompts to get even more cost efficiency from the SERV Reasoning engine. - Prompt Guard: Security and privacy are minimums for any infrastructure running in high-stakes environments like banking. Protecting against prompt injection attacks, Prompt Guard's built-in security layer protects AI agents and the trillions of dollars they manage. We are excited about the launch of SERV v2, and can't wait to announce the exact launch timing. The future is bright.

SERV is becoming the reasoning layer enterprises, banks, and governments run their agents on. SERV v2 lands mid-July: Multipath Reasoning, Shadow Agents, Verification Hints, Prompt Guard, and Benchmark Tooling. This is how trust gets built.

SERV Summer 🏝️