00 — Private AI, end to end
We design, deploy and maintain AI systems on infrastructure you own or exclusively control — so your data answers questions without becoming somebody else's data.
01 — What we build
We select and run open, self-hostable models sized to the job — a capable general model for language work, smaller specialised ones for extraction and classification. Deployed on your hardware, your cloud account, or a dedicated environment, with the licensing and fallback paths documented in writing.
Policies, contracts, catalogs, tickets, email archives: ingested into a retrieval pipeline on your infrastructure, so answers quote your documents instead of a model's guesses. Stays current on a refresh schedule you set.
AI wired into the systems you already run — helpdesks, ERPs, CRMs, inboxes — for the boring middle of operations: summarise this thread, draft that reply, extract these fields, flag that outlier. Human approval stays in the loop wherever mistakes are expensive.
A private AI is only worth what you can verify. We build a test set from your real cases, measure accuracy and failure modes before go-live, and keep measuring after it — so "the AI is wrong sometimes" becomes a number, with a plan.
Access controls, logging, encrypted storage and backups from day one, plus the unglamorous work — monitoring, patching, cost and capacity planning — that decides whether your AI is still excellent in two years.
02 — Where it runs
Servers in your rack or colo. Nothing leaves the building. The right choice when confidentiality is contractual, latency matters, or the workloads are constant enough that owning the hardware pays for itself.
Dedicated GPU capacity inside your cloud account, isolated and monitored. Still your keys and your billing — often the fastest way to start, with costs that stay visible and yours.
Some workloads — cutting-edge multimodal tasks, niche capabilities — may still justify a managed API. Where that’s true we’ll say it, fence it off narrowly, and keep the sensitive path on your infrastructure.
03 — Common questions
Not in the architecture we build. The model runs where you run it, reads only what you let it read, and every access path is logged. What we can't promise for managed APIs you adopt elsewhere — which is exactly the problem we're here to solve.
For the vast majority of business work — drafting, extraction, retrieval-grounded Q&A, classification, summarisation — current open models, tuned to your domain, are more than adequate and sometimes better than generic frontier access, because they answer from your documents rather than the open web. Where they genuinely aren't, we tell you before you commit.
It can be, at low or spiky volume — that's real. Ownership wins when usage is steady and privacy matters: a known monthly cost you control beats a metered one you can't predict, and the comparison should be made with your actual numbers during discovery, not with anyone's brochure.
The system runs without us. That's the whole point of ownership: documented deployment, standard components, no proprietary glue only Dircel understands. If you'd rather never think about it, we can stay on to operate it — but you're never hostage to that decision.
Either us, or people we help you hire — see Recruitment. Owning an AI without staffing it is how private AI projects die quietly, so we treat the two as one project.
04 — Next step
The fastest way to find out whether a private AI can work for you is a pilot on one real, painful slice of your operations.
Talk to Dircel →