Zero Silos helps midsize organizations (for-profit, nonprofit, and government) choose the right AI tools and train their teams to use them well. We follow the entire AI landscape so you do not have to, and we turn it into clear, practical guidance you can act on.
AI rarely stalls on the technology. It stalls in the silos: scattered data, disconnected tools, teams left to figure it out alone. We close those first. Then, as one focused team keeping pace with a fast-moving field, we turn it into clear guidance, the right tools for your workflows, and training that helps your people actually use them.
A company is only as ready as its data. Before any tool or automation, we assess where your data lives, how it is organized, and how accessible it is to AI. If your information is scattered across disconnected systems, we give you a clear plan to consolidate it into one or two accessible places, so you have a foundation AI can actually work with. We make sure midsize organizations are genuinely ready before they invest.
The AI landscape changes every week. We study and test the new tools as they are released, track what is actually working, and translate it into plain guidance for your team. You get clarity instead of noise.
We build on Claude by default for the work that demands the most care, where our depth runs deepest, and we stay genuinely independent, recommending the AI tools that fit your needs, workflows, and constraints, with an honest view of where each helps.
Adopting a tool is not the same as getting value from it. We provide hands-on training that helps your people use AI effectively in their day-to-day work, so the investment pays off in practice, not just on paper.
Our work does not end at handover. Once your systems and tools are in place, we remain available for ongoing support as needed, from troubleshooting and tuning to training new staff and advising as your needs change. You implement once and have a partner you can call.
Zero Silos keeps a constant read on the AI market so our clients can skip the noise. We study and test the major tools as they evolve, then recommend the right combination for each organization's ecosystem, data sensitivity, and team readiness. We lead with Claude for the work that matters most, while staying genuinely independent about the rest.
The strongest organizations do not pick one tool. Most run two or three together, matched to the job. Our value is helping you choose the right combination, then training your team to use it well. For regulated clients, where data is processed matters as much as which model is used, so we lead with governed, enterprise-grade platforms rather than consumer apps.
The right tool isn't the newest one. It's the one your team will actually use, on data you can trust.
AI is only as good as the data beneath it. Before we advise on AI, we make the foundation trustworthy. A small sample, anonymized under NDA at our clients' request.
A senior, partner-led team that built and ran these systems before advising on them, grounded in real experience, not theory. You work directly with that senior team, whose backgrounds span federal and enterprise finance-systems modernization, AI architecture and engineering, and audit-ready operations. The common thread across that work is simple: clear the silos, so an organization's data, tools, and people finally pull in one direction. AI consulting is the whole job, so we stay current as the field shifts. We take on a deliberately small number of engagements at a time. We are not the largest firm in the room, and we are not trying to be. We are the firm you call when the work has to be done precisely, by people who have done it before.
We design, build, and supervise production AI systems on Claude, from first workflow to operator hand-off.
More than 30 years of combined experience across AI architecture and engineering, IT development, and finance systems in complex organizations.
Audit-ready operations, reporting, and reconciliation experience that grounds our advice in how the work really runs.
We recommend what fits your needs, not what we are paid to sell.
Zero Silos is a member of Anthropic's Claude Partner Network, building on Anthropic's partner training and implementation standards. We are also a member of the OpenAI Partner Network, which keeps our advice independent.
Short memos on what we are learning in production. Plain language, for operators and leaders.
Our principles for recommending AI that is simple enough to fit the problem, transparent enough to trust, and yours to keep, without locking you to a single vendor.
Read the memo →Why teams stop using the AI they paid for, and what actually turns access into a habit that sticks past the first month.
Read the memo →What happens to the headcount plan when work stops scaling with volume, and what that means for teams that cannot simply hire their way out.
Read the memo →Why the most consequential choice in any AI system is not the model, but how and when human judgment is invited back in.
Read the memo →A short framework for telling structural investment apart from fashion spend, for any organization deciding where to put its first AI dollars.
Read the memo →We are launching a channel where we talk about how we implement AI, how we choose the right AI for different companies, and what tools are out there. We keep ourselves up to date so you don't have to. Just practical guidance from the work we do.
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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 is as Claude Solution Architects, 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.
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.
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.
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.
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.
