Agent Infrastructure for Enterprises, Governments, and the Autonomous Economy.
London, England
- Tweets 2,810
- Following 100
- Followers 22,698
- Likes 1,004
Pinned Tweet
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.
45
94
352
119,957
SERV Reasoning v2.0 Release
Launching mid-July, SERV v2 is the most significant upgrade we've ever done to the SERV Reasoning engine.
Our goal remains the same: SERV becomes the foundational AI agent infrastructure that enterprises, global financial institutions, governments, and humanoid robotics companies use to run AI agents at scale.
We believe the lack of enterprise trust in AI agent reasoning is the #1 barrier holding back the mass adoption of AI agents in high-stakes industries like banking, robotics, and government workloads. That's why the enhancements in SERV v2 focus on making AI agents more trustworthy, reliable, and more cost-efficient than ever before: exactly what our target customers require.
We are going to be explaining the architecture of each feature in more detail over the coming weeks.
Here is what SERV v2 update enables:
- Multipath Reasoning: This foundational upgrade changes the core of the SERV Reasoning engine. Decision making in the real world is complicated, messy, requires orchestration among multiple actors, and can be contradictory. The same will be true when enterprises implement fleets of AI agents at scale. Multipath Reasoning allows complex decision trees with contradicting rules to coexist in one reasoning graph, upgrading the ability of AI agents on SERV to reason through complicated real-life situations.
- Shadow Agents: With the goal of increasing the reliability of outputs to 100% - a baseline requirement for high-stakes environments - Shadow Agents are separate verification agents paired with the main agent. They review every draft against the original brief before anything ships. Missed requirements get caught and rewritten, and only the version that passes gets delivered - preventing errors from poisoning downstream outputs.
- Verification Hints: To reduce re-work, cut costs, and increase the accuracy of outputs as we work towards our goal of 100% reliability for enterprise applications, AI Agents will now be able to receive extra signal about what a correct output should look like before they produce one.
- Benchmark Tooling: Potential enterprise customers can now see the cost savings and reliability improvements of switching to SERV on their own workloads before integration. For existing enterprise customers, their engineering teams can optimize existing prompts to get even more cost efficiency from the SERV Reasoning engine.
- Prompt Guard: Security and privacy are minimum requirements for any infrastructure implemented in high-stakes environments like banking and financial services. Prompt injection is a serious risk for banking AI agents handling trillions of dollars. Prompt Guard's built-in security layer protects AI agents from injection attacks.
SERV v2 goes live mid-July with all of these upgrades.
Each element in SERV v2 solves an issue that's preventing the adoption of AI agents within enterprises, financial institutions, governments, and fast-growing markets like humanoid robotics.
Multipath Reasoning lets agents work in the real world. Shadow Agents and Verification Hints increase reliability. Benchmark Tooling increases cost efficiency and brings new customers through the door. Prompt Guard increases security and privacy.
79% of enterprises need to adopt AI agents in some form (PwC), and SERV v2 enables them to run those agents on OpenServ.
The future is looking bright.
34
71
265
55,942
Claude Sonnet-5 was just integrated into the SERV Engine.
On its own, Sonnet-5 is mid-tier. With SERV, it jumps into the top cluster, matching Opus-4.8 at half the cost.
That's the whole vision of SERV - to make all models reliable and accessible.
The frontier labs keep shipping raw intelligence. SERV turns any model, even a small one, into reliable, production-ready performance anyone can afford.
Now available to Private Beta builders. Apply below ↓
8
39
191
6,207
OpenServ retweeted
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.
45
94
352
119,957
As part of our ongoing plans to integrate with @base ecosystem & Base AI scene, we have migrated protocol assets to Aerodrome incentivized pools.
$SERV pools on Base are now eligible to receive emissions on AERO: deeper liquidity, lower slippage, and better capital efficiency.
(Information for SERV holders: There is no action required for $SERV token holders, there is no new token)
16
32
210
20,964
OpenServ retweeted
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!
9
15
73
6,270
The market is looking for answers to the exact issue SERV is solving: exploding inference bills and unreliable agents.
Our BD team is in San Francisco this week for AI Engineer World's Fair, meeting decision-makers & builders.
SERV is the solution they're looking for.
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!
8
30
162
8,123
SERV is moving deeper into the most demanding, regulated industries.
We've signed an agreement with Vanta.com to pursue certifications needed in high-stakes environments.
These unlock pilots across banking and fintech - a market expected to reach $460B in 2026.
16
55
246
31,704
As the industry is hunting for the fix, SERV already has it - and we're moving fast.
Routing is a band-aid. Even with a strong model planning, an unreliable implementer breaks it.
The real answer: make small models smarter. SERV does this for any LLM, cutting costs up to 100x.
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.
7
37
167
8,777
SERV is designed to free enterprises from depending on any government’s rules.
US Commerce directives pulled Fable 5 and gated GPT-5.6.
SERV allows open-source agents, like DeepSeek, to match those models at 90x lower cost.
No government can cut off what you build on SERV.
SERV Reasoning just took GLM-5.2, one of the strongest open models ever built, and immediately cut its failures by 22%.
That's just v1. Every step on our roadmap brings us closer to the goal of perfect, deterministic reliability: agents that are 100% right.
v2 is next: Shadow Agents, pushing agent reliability much further. Then, Graph Sharding and Private Inference; releases that make SERV deployable inside a bank - fully auditable, fully secure.
GLM-5.2 is also proving to be difficult to steer, with a very similar signature to Fable-5. The pattern is clear: frontier labs are trading control for raw intelligence.
You can't trust AI you aren't able to control - that is the key bottleneck to solve, preventing adoption of agents in businesses and governments.
It's the real prize we are after - not consumer chat, but the moment reliable agents move into enterprise at scale.
5
25
170
9,954
SERV is moving to the core of how governments adopt AI.
We just got the latest data from Neol, on how their agents went from 50% to 100% reliability with SERV - live with the UAE government.
Neol was the first stage. Building on what we learned, we're now in active talks with institutions and enterprises across the US, Europe, Africa, and the Middle East - all wanting to run agents in regulated environments only SERV can unlock.
Full case study soon, mapping where SERV goes next across the highest-stakes AI markets on earth - and the technical roadmap that gets us there.
12
43
217
6,610
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.
11
39
188
7,233
SERV aims to anchor AI adoption across banks, governments, and regulated systems.
Our infrastructure is proven in high-stakes decisions, making agents auditable, reliable, efficient and secure.
Next - SERV Reasoning v2 - a new engine generation, strengthened across all four.
4
33
203
5,568
