Washington, DC, July 29, 2026 — Zero Silos, an AI consulting and advisory firm that helps regulated and process-heavy organizations put AI into daily production, today announced that it has been named an OpenAI Select Partner within the OpenAI Partner Network.

The OpenAI Partner Network is a global program for partners to build, sell, and deliver AI solutions with OpenAI. It brings together partners with deep industry expertise, delivery capabilities, and customer relationships while equipping them with resources, enablement, and support to help enterprises adopt OpenAI frontier models and products and turn them into measurable impact.

As an OpenAI Select Partner, Zero Silos will continue working with OpenAI to help organizations build, deploy, and scale AI solutions responsibly and effectively. This work will help organizations get more useful work from every token and stronger performance per dollar with GPT-5.6, while using ChatGPT Work to turn ambitious goals into finished work. Zero Silos pairs hands-on AI engineering with the governance and adoption work that makes deployments stick: signed ground rules, human sign-off on consequential output, staged rollouts, and role-based training on each team's real work.

"Being named an OpenAI Select Partner confirms the way we already build: production AI on the client's own OpenAI account, with governance a regulator could read and adoption people actually sustain. We look forward to bringing OpenAI's partner enablement to every engagement and helping more organizations turn AI ambition into finished, measurable work," said Alexander Biniam, Founder and CEO, Zero Silos.

Zero Silos supports organizations across financial services, healthcare, life sciences, and the public sector, primarily in North America, with AI readiness assessment, governed workspace deployment, agent engineering, and monthly managed maintenance. Its work includes governed production deployments of 200 and 325 daily users at enterprise clients and a predictive operations system that processes roughly 60,000 network events per day to give a network operations center days of early warning before hardware failures.

Looking ahead, Zero Silos plans to deepen its OpenAI enablement through PartnerU badging across its delivery team and to expand governed OpenAI deployments with current and new clients, helping customers translate AI ambition into business outcomes.

Learn more about the OpenAI Partner Network: openai.com/business/partners

Media contact: Zero Silos · info@zerosilos.ai · Northern Virginia and Washington, DC

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Washington, DC, July 15, 2026 — Zero Silos, an AI consulting, advisory, and training firm that helps regulated and process-heavy organizations put AI into daily production, has joined Anthropic's Claude Partner Network.

Anthropic describes the Claude Partner Network as its program for organizations bringing Claude to enterprises, providing partner training, technical support, and joint market development, with a partner hub where members track program requirements. Zero Silos joined the program in July 2026.

As a member of the Claude Partner Network, Zero Silos helps organizations put Claude to work on the tasks where judgment matters. Which model carries which task is a deliberate choice rather than a default: the reading, reasoning, and drafting a consequential decision rests on goes to a model chosen for depth on that kind of work, while that same work at everyday volume, and high-volume well-defined steps, can go to a lighter and faster one. A person stays accountable for anything consequential, whichever model produced it.

Zero Silos pairs those model choices with the governance and adoption work that makes deployments stick: signed ground rules, human sign-off on consequential output, staged rollouts, and role-based training on each team's real work. Those are the same four commitments the firm brings to every engagement, whichever model is underneath. In a regulated setting the choice of model also turns on data handling and retention terms, not capability alone, and those terms go into the signed ground rules before anything goes live.

"We use Claude two ways, and they are the same work: we build the agents that carry a client's production load, and we train the people who have to live with them. We do both against Anthropic's own guidance on how these models are meant to be used, rather than against our reading of it, and the partner network is how we stay current with it. Our clients are not asking us to pick a winner. They are asking what to put in front of their teams on Monday." Alexander Biniam, Founder and CEO, Zero Silos

Zero Silos works across more than one AI ecosystem by design, which is what lets the firm say plainly where Claude is the right answer and where it is not.

The firm's senior team comes out of federal network operations and institutional finance. Zero Silos works with midsize for-profit companies, nonprofits, and government organizations, with deep experience in financial services and the public sector, and supports them with AI readiness assessment, AI advisory and tool selection, workflow and systems integration, hands-on team training, and ongoing support. Engagements are organized around The AI Workspace, a set of plain-language working files the client owns and keeps, so the workflow survives the end of the engagement. The firm's training program, AI for Your Team, is taught by a Zero Silos instructor who works with Claude full time, using the team's own work.

Looking ahead, Zero Silos will draw on Anthropic's partner training resources in its AI for Your Team program as they become available, and bring governed Claude deployments to more of the teams it advises.

Learn more about the Claude Partner Network: claude.com/partners

About Zero Silos. Zero Silos is an AI consulting, advisory, and training firm that helps organizations put AI into daily production. The firm works with midsize for-profit companies, nonprofits, and government organizations, is based in Northern Virginia and Washington, DC, and serves clients across North America. More at zerosilos.ai.

Media contact: Zero Silos · info@zerosilos.ai · Northern Virginia and Washington, DC

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Before we design anything, we ask one question: what is the simplest setup that actually fits this problem? It's an unfashionable question. Most AI being built today is over-built, and the cost of that shows up later as systems no one can see into, that break easily, and that you can never run without the people who built them.

We work from a few plain principles. They're what's left after years of building systems in places where being wrong is expensive.

Match the tool to the work. There are powerful, complex AI setups for problems that are fast-moving and unpredictable. But most of the work that actually matters inside an organization isn't like that. It's step-by-step, it needs a person to check each step, and it runs again next week. For that work, an elaborate system is more moving parts than the job needs, and the moving parts are the part that breaks.

Make it a glass box.

We don't build systems you can't see into and then bolt an explanation on top. We build systems that are readable from the start. The cleanest version we've found is almost embarrassingly simple: organize the work as a set of named, plain-language files a person can open at any time, and have each step write down what it did in plain words. There's no separate "explain it later" layer to build, because the work explains itself. For anyone who will ever sit across from an auditor or a regulator, that isn't a nice-to-have. It's the whole point.

Keep a person at the gate. Between each step is a point where a person reads what the system did and decides whether it goes forward. That's where human judgment re-enters, and it's what lets an organization stand behind the result. Oversight expectations are only getting stricter; we build for that from day one, not in a panic later.

Build it so you own it. The difference between a tool and a dependency is whether you can run it without us. We'd rather hand you something your own people can change with a few keystrokes than a system that comes with a permanent support contract. The method isn't tied to one vendor: we build on Claude by default, because that's where our depth runs deepest, but the same setup runs on whatever model you prefer. The work, and the result, stay yours.

Stand it on data you can trust. None of this works on bad data. The least glamorous part of every project, getting your core records accurate and your systems genuinely talking to each other, is the part that pays off for years. We spend on the things that make every future project cheaper, and we're suspicious of anything that only looks good in a demo.

Good design is mostly restraint. The simplest system that fits the problem, that a person can see into, that your team can own, is almost always the one still running a year later, after the impressive ones have been quietly switched off.

If that's the kind of system you want, it starts with a 30-minute conversation.

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The most common AI problem we're called in to fix isn't a broken system. It's a paid-for tool that almost nobody uses. The licenses are active. The demo went well. And three months later, usage is a handful of curious early adopters and a long tail of people who tried it once and went back to the old way.

Buying access to AI is not the same as adopting it. Access is a purchase. Adoption is a change in how people work, and people don't change how they work because a tool appeared. They change when the new way is clearly easier for the specific job in front of them, and when someone they trust has shown them how.

The blocker is almost never the technology. It's the habit.

Most rollouts fail in a predictable way. The training is generic ("here are ten prompts to try"), disconnected from anyone's real work, delivered once, and then everyone is left to figure out the hard part alone. People are busy. Faced with a blank box and no clear link to their actual tasks, they reasonably decide it isn't worth the effort, and the habit never forms.

What actually works is narrower and more human. We train people on their real work, not toy examples: the report they actually write, the inbox they actually triage, the review they actually do. We pair the first attempts with supervision, so people build confidence on real tasks without fear of getting it wrong. We redraw the workflow so using AI is the path of least resistance, not an extra step bolted onto the old process. And we give people explicit permission and a clear judgment role, because most people won't lean on a new tool until they're told it's expected and shown where their own judgment still matters.

The measure of a rollout isn't how many people attended the training. It's how many are still using the tool, on their own, sixty days later, and whether the work is actually better for it.

That's the gap we close. Not "here's a tool," but "here's how your team makes it part of the job, and keeps it there." If you've bought AI and adoption has stalled, that's exactly the conversation to have.

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For thirty years the assumption in most back offices was simple: more volume means more people. Twice the claims, twice the clerks. Twice the reconciliations, twice the analysts.

The headcount line and the workload line climbed together, and almost nobody questioned the slope. That assumption is now quietly breaking.

Put a supervised AI workflow against high-volume, rule-bound work, reconciliations, intake, exception handling, first-pass review, and the link between workload and headcount stops being a straight line. Volume can double while the team stays flat. For a company, that shows up as margin. But many of the organizations we work with, including non-profits and public agencies, rarely get to think about margin. They think about coverage.

This is the part that gets missed.

When you can't simply add staff, because the role isn't budgeted, hiring takes months, or the budget was set two cycles ago, automation isn't a cost-cutting story. It's the only way to take on work you were already failing to cover.

So the right question isn't "how many people can we remove?" It's "what should our people be doing that they've never had time to do?" The answer is almost always the same: judgment. Exception handling. The hard five percent of cases that don't fit the rule, the work that was always supposed to be the job, before the job became data entry.

The organizations that handle this well do one thing differently. They redraw the role before they deploy the system, not after. They decide, in advance, that a person's job is now to supervise, to catch the edge cases, to own the record of what happened, and they retrain for it. The ones that handle it badly automate first and let the org chart sort itself out, which it never does.

The hiring curve is ending. What replaces it isn't a smaller team. It's a different one. If your team is hitting that wall, we should talk.

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Every conversation about an AI system starts in the wrong place. Which model. How big. How clever. We've learned to let that conversation happen, and then quietly change the subject.

Because in every system we've put into a regulated, high-stakes, or financial setting, the model was never the hard part. The hard part, the part that decides whether the thing survives its first review, is supervision. Where does human judgment re-enter? When? On what signal? And can you prove, six months later, exactly who decided what, and why?

A system that's right 95% of the time isn't a product. It's a liability with good marketing.

The product is the other five percent: the design that catches the uncertain case before it becomes a decision, hands it to a person, records the override, and learns from it. The model is the easy, commoditized layer. The supervision layer is where the expertise lives, and where the value is.

This is doubly true in the settings we work in. An automated workflow that can't explain a change it made is worse than no automation at all. A financial close that an AI touched but can't account for won't pass. In these worlds, "it usually works" is not a sentence anyone wants to say to a regulator, an auditor, or a board.

So we design backwards. We start with the record of what happened and the path for a human to step in, and we build the automation to fit them, not the other way around. We decide what a person must see before the system is allowed to act, and we make that line explicit, logged, and reportable.

The uncomfortable truth is that this is unglamorous work. Nobody demos an override log. But the override log is the reason the system is still running a year later, when the demo-driven projects have all been quietly switched off.

Supervision isn't a constraint on the product. Supervision is the product. If you're putting AI near a decision that has to stand up to scrutiny, this is the conversation to have first.

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We get a version of the same question in almost every first meeting: where should we spend? Usually there's pressure behind it, a mandate from above, a budget that has to be committed, a sense that everyone else is moving faster. Here's the honest answer we give, before anyone has signed anything.

Most AI spending right now is fashion spend. It buys a demo, a press line, and a system that doesn't survive contact with the actual work. It's real money spent to look modern. The buying cycle rewards it, because a demo is easy to fund and a habit is hard to measure.

Structural investment is different, and it's boring. It's the clean data. The connected systems. The core records that are finally accurate. The numbers that finally reconcile. These things don't demo well. They compound. The organization that spends two quarters making its data trustworthy will, in year two, be able to do things the flashier one next door still can't, because the flashier one built on sand.

So we tell leaders to ask three questions of any AI investment.

First: if the model vendor disappeared tomorrow, what would we still own? If the answer is "nothing," it's fashion spend. Second: does this make the next project cheaper, or only this one? Structural investments lower the cost of everything that follows. Third: can our own people run it in ninety days without us? If not, you haven't bought a capability. You've rented a dependency.

None of this is the answer people are hoping for. The hope is that there's a product you can buy that makes the problem go away. There isn't. There's only the slow, unglamorous work of making your systems tell the truth, after which the AI part is almost easy.

Spend on the things that compound. Be suspicious of anything that demos beautifully. And never buy a capability you can't eventually run yourself. Before your next AI investment, it's worth a second opinion.

Book a 30-minute consultation →