Local & Physical AI
Your AI doesn't have to live in someone else's cloud.
Run the routine work on hardware you own, send only the hard problems out, and put AI where the work actually happens — in the office, in the warehouse, in the field. We size it, build it, and stand it up, whether that's a box in your server room, a locked rack in our facility, or a camera watching the line.
The question isn't cloud versus local. It's which work goes where.
Most businesses send every AI task to a frontier model in the cloud. That's the right call for hard problems and the wrong call for everything else. Get the routing right and you cut AI spend substantially without losing quality on the work that matters.
Local, small model — high volume, low judgment
Classification, tagging, extraction, summarizing, drafting boilerplate, redacting documents before they ever leave your network. Runs on hardware you own. Marginal cost per task: effectively zero.
Mid-tier cloud model — the daily workhorse
Client-facing drafts, research synthesis, most agent steps. Cheap per call and good enough for the majority of your workload.
Frontier model — the hard 10%
Complex reasoning, code you'll actually ship, anything where a wrong answer is expensive. Use it deliberately, not by default.
Most teams run 100% of their work at tier 3. Moving even the routine half down a tier is the single largest AI cost reduction available to a small business — and it usually feels faster, because smaller models answer quicker.
Agents are why this matters now.
An agent task isn't one call — it's a loop: plan, call a tool, read the result, retry, re-read the whole context, try again. A single agent run can consume more tokens than a hundred chat messages. Run that inner loop on a small local model and escalate to a frontier model only at the decision points, and cost drops by an order of magnitude while the output stays the same.
Price your agents by the loop, not by the prompt.
Be honest about whether you need this.
Local hardware earns its keep when
- You have a high-volume, repetitive AI task running daily — not occasional ad-hoc use
- Your data can't leave your network: client records, PHI, legal files, unreleased IP
- You're paying for the same routine work over and over, month after month
- You want a fixed cost you own rather than a usage bill that scales with adoption
- Someone can own the box — this is real infrastructure, not a subscription
- Latency matters, or the work has to keep running when the internet doesn't
Stay on the cloud when
- Usage is light or unpredictable — cloud APIs are cheaper below a real volume floor
- You need frontier-level reasoning for most tasks; open models don't match the top cloud models on the hardest work
- Nobody owns the maintenance — an unmaintained box is worse than no box
- You're still deciding which workflows to automate; buy hardware for a proven workload, not a hypothesis
We'll tell you when the answer is “don't buy anything.” That's a real outcome of a sizing conversation and it costs you nothing.
Three ways we handle this for you
Clean Room— sensitive work, nothing left behind
When the job is sensitive but occasional, you don't need to buy hardware — you need an isolated place to do it once. Clean Room spins up a dedicated GPU instance in a private subnet with no internet access, runs open models against your documents, and cryptographically erases the storage when the session ends. Diligence, audits, investigations, contract review: burn-after-reading analysis, without the data ever touching a public AI service.
See Clean Room →Turnkey build— we spec it, build it, and stand it up on-site
If you'd rather own the whole thing, we do the build end to end. We size the box against the work you actually run, source the hardware, install the right open models for it, wire it into your network and the tools your team already uses, and set up the routing so routine work stays local and the hard 10% still goes to a frontier model. Your team gets a working system and the documentation to run it — not a parts list. Nothing leaves your premises, there's no per-token bill, and the models are yours to keep running whether or not anything changes upstream.
Colocation— own the hardware, skip the closet
If local AI makes sense but a server in the back office doesn't, we'll host it. Agents of Work operates out of a secure co-location facility with dedicated locked racks, private networking, redundant power, and 24/7 physical security — the same facility that has hosted infrastructure for Fortune 500 companies. You own the hardware and the models; we rack it, network it, secure it, and keep it running.
Physical AI
AI that sees, hears, and acts where the work happens
Not every problem is a document problem. Plenty of the expensive ones happen on a floor, in a bay, on a job site, or at a counter — and they never reach a screen until someone writes them up hours later. Physical AI closes that gap: a model running on hardware at the location, watching or listening to the real process, acting on what it finds in the moment.
Because it runs at the edge, it works the way the site works — no round trip to a data center, no outage when the connection drops, and no video of your operation sitting on someone else's server.
Vision on the line
Cameras plus a local model watching for defects, safety violations, PPE compliance, stock-outs, or process drift. Alert in seconds, not on the next audit.
Sensor & equipment integration
Pull from PLCs, meters, scanners, and IoT sensors; catch the anomaly before it becomes downtime; write the result back into the systems your team already uses.
Edge boxes in the field
Vehicles, trailers, remote sites, and anywhere the network is bad. The model runs locally and syncs when it can.
Voice & kiosk
Hands-free capture for people who can't stop to type: intake at the counter, inspection notes in the bay, order entry on the floor.
How it connects back: the same routing logic applies. The edge device handles the constant, high-volume perception work. Anything ambiguous escalates — to a bigger model, or to a person. And the events it produces feed the same automations and reports as the rest of your stack.
Honest limits. Physical AI is a real integration project, not a subscription. It needs a site walk, cameras or sensors placed properly, a network that reaches them, and someone on your side who owns the rollout. We scope it in phases and prove it on one line, one bay, or one door before anything gets rolled out across the operation.
How an engagement runs
- 1
Sizing call (free)
What you want to run, who runs it, and what the data rules are. You leave with a hardware recommendation and a cost range whether or not you buy anything.
- 2
Proof on one workflow
One line, one bay, one document type. A success metric agreed up front, and a short window to hit it.
- 3
Build & install
Hardware sourced and configured, models installed and tuned to the box, routing wired up, integrations connected to your existing tools.
- 4
Handover
Runbook, documentation, and training for whoever owns it on your side. Support and monitoring available if you'd rather we keep the pager.
What it costs, directionally
A capable single-box setup — a 128GB unified-memory mini-PC, a Mac Studio, or a single-GPU workstation — runs roughly $1,700–$5,300. A workstation that serves a whole team on larger models is closer to $13,000–$16,000, and multi-GPU servers start around $60,000. Against that, compare only the routine, high-volume tier of your cloud spend: if that portion sits consistently in the low hundreds of dollars per month, an entry-level box pays for itself inside a year and keeps paying after.
These are planning ranges for hardware, not project quotes — integration, models, and physical installation are scoped separately.
Size it before you buy it — free.
Our Local Model Advisor asks what you want to run and how many people will use it, then tells you which open models fit, what hardware they need, realistic tokens per second, and what it costs.
Open the Local Model Advisor →Questions we get
- Will an open model be good enough?
- For the routine tier, yes — and that's most of the volume. For the hardest 10%, no, which is why we route that to a frontier model instead of pretending otherwise.
- What happens when better models come out?
- You swap the weights. That's the point of owning the box — you're not locked to one vendor's roadmap or pricing.
- Who maintains it?
- You, with the runbook we hand over, or us on a support agreement. What doesn't work is nobody.
- Can we start without buying hardware?
- Yes — Clean Room for sensitive one-offs, or a cloud pilot that proves the workflow before you commit to a box.
- Do you work with our existing IT or MSP?
- Yes. We scope around them, not over them.
Tell us what you're trying to run.
Two ways in: size the hardware yourself with the advisor, or send us the shape of the project and we'll come back with a scope and a number.