Most AI consultants run your business through someone else's public cloud — your data passing through models you can't see, on terms you don't set. plainworks runs sensitive workflows on its own secure, private AI inference and serving infrastructure, built and managed by Nathan. This is the part most studios can't offer, and won't talk about.
Sensitive workflows can run on plainworks-controlled infrastructure. When an external API is the right tool, you'll know why, what data is sent, and what the fallback is.
No black boxes, and no claims we can't back. That second sentence is the part you won't hear from most vendors — because most of them route everything through a public API and hope you don't ask. We'd rather tell you exactly how your work runs.
We match the deployment to your sensitivity, budget, and control needs — and we tell you the trade-offs of each in plain terms.
Our own secure inference and serving stack. The most private option — your sensitive workflows never touch a shared public cloud, and the hardware is operated in-house.
A dedicated, access-controlled cloud environment when you need elastic scale with stronger isolation than a shared public endpoint.
The model runs on hardware in your building, behind your network. For workflows where data simply cannot leave the premises.
Most engagements use local-first routing: we keep the work on plainworks-controlled infrastructure by default, and reach for an external model only when it's the right tool — disclosed and approved, never silent.
Specs evolve as the cluster grows — this reflects the current production environment.
It isn't used to train anyone else's model. Your workflows, metrics, and customer details stay private.
Sensitive work runs on plainworks-controlled infrastructure — not a shared public API by default.
Every external model call is disclosed: what's sent, why, and the fallback. You approve the boundary.
The stack is built and operated by Nathan — not a reseller badge, not an outsourced ops team you'll never meet.
Owning the infrastructure is the easy headline. Real trust comes from managing risk across the whole lifecycle of a system — the way mature AI risk frameworks describe it. We design for that, not just for a server in a rack.
Scoping what data a workflow actually needs — and excluding what it doesn't — before a line of it is built.
Testing outputs against real cases, with a human in the loop where judgment matters.
Clear boundaries on storage, retention, and who can see what — written down, not assumed.
Monitoring in production, with the ability to roll back, retrain, or pull a model if something drifts.
For regulated industries, we'll talk openly about a readiness roadmap — the controls, contracts, and scope it would take to operate there. What we won't do is slap a compliance acronym on a slide before it's legally and operationally true.
If a vendor leads with a certification logo on day one, ask them to show you the controls behind it. We'd rather earn the claim than borrow it.
The goal isn't to cut your people. It's to take the operational drag off them so they spend time on the work only humans can do.
More room for the relationships, the craft, and the specialty you actually got into business to do — without scaling the hours to match the growth.
Right-sized models on efficient infrastructure — we don't burn a data center's worth of compute to send a follow-up email.
Every system has to earn its place by closing real profit leakage. If it doesn't pay for itself, we don't build it.
Tell the consultant what you're working with. We'll talk through where it should run, what it would take, and where the honest limits are — before anyone signs anything.