Private AI Infrastructure
Not every task needs ChatGPT, or a server in your office. Cortex sends routine work to private models, uses simple rules where AI isn't needed, and calls a cloud model only when the job needs one.
We decide this after we've seen the work. Hardware is a workload decision, not a line item we add so the proposal looks heavier.
Fast to start. Fine for many first deployments, two follow-up paths, a morning summary, no collision files leaving the building. You do not need a server room on day one.
Dedicated hardware you own or control. More privacy for customer and job files, more predictable cost for work that runs every night whether anyone is in the building.
Usually the practical answer. Local for the overnight watch and the briefing. Cloud when a job needs more. Rules when a model is not needed at all.
Classify the work. Put continuous jobs where cost is stable. Keep sensitive records closer to the shop when it matters. Connect the same way we always connect.
Every task is one of three things: a simple rule (“repair order older than three days, no note”), a small writing task (draft the morning briefing), or a hard task (a messy email that needs a stronger model). Rules do not need AI. Small tasks can run locally. Hard ones can go to the cloud.
Overnight checks and morning briefings run whether anyone is in the building. Paying per use for cloud AI is a bad fit if it is the same job every night. Dedicated hardware you control makes that cost predictable. Occasional hard work (a tough customer email at 2pm) can still use a capable cloud model.
Customer names, repair order notes, collision files, invoices. Some owners do not want that leaving the building for a routine check. Private or hybrid setups keep more of that data on hardware they own. Details depend on each setup. We do not pretend every company needs a server rack.
APIs, approved integrations, webhooks. For Detail Driven, collision data came through a local CCC ONE EMS export, files CCC already knows how to create. We do not scrape private databases to make a privacy story true.
Sending every ticket to the largest cloud model is how you buy a surprising invoice and a privacy argument.
Sending every ticket to the biggest cloud model is how you get a surprise bill and a privacy argument. Match the model to the task, because the work is not all the same.
A server room does not make the company serious. It is the right answer when privacy, round-the-clock workload, or cost call for it. Plenty of first builds start in the cloud. The audit says which.
Local for the overnight checks and the briefing. Cloud when a job needs more. Rules when AI is not needed at all. Designed around your work, not around an AI company’s keynote.
Infrastructure
Routine work can stay on hardware you control. Hard reasoning can still call a cloud model. Rules when AI is not needed at all.
$ █
without cortex · someone still has to remember to look
with cortex · the layer watches, drafts, and waits for you
Detail Driven runs dedicated Linux on site. We also tell operators when a rack would be theatre.
Detail Driven · infrastructure
When we tell you not to buy hardware
We design the architecture around privacy, performance, reliability, and cost, not around a specific model provider.
More of the stack sits on infrastructure you own. Routine inference does not have to leave the building.
Sensitive records can stay closer to the shop. Collision files and RO notes are not a public demo.
Reduce variable inference costs for high-volume, every-night workloads. Burst jobs can still call the cloud.
A hard reasoning job can still go to a capable cloud model. Private does not mean nothing ever leaves.
No. Many companies start in the cloud. A private server is an option when privacy, cost, or control make it the better fit. The audit says which.
No. Hybrid is common. Routine jobs stay local. A hard reasoning job can still go to a capable cloud model. You decide what is allowed to leave.
Agents only see the systems and records you approve. Private or hybrid setups keep more of the work on systems you control. Training policy is set per build. By default, we do not train a public model on your repair orders.
Cloud, private, or mixed. We'll say which fits your workloads, privacy needs, and cost.