On-premise AI that runs inside your environment.
We build AI agents that run inside your environment — your servers, your cloud, next to the ERP you can’t move. Your data never leaves.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
Every AI tool assumes your data can move. Yours can’t.
From a transformation lead at a European manufacturer: SAP S/4 on-prem, decades of history, data-residency rules — and a market full of AI demos that assume clean SaaS data.
Cloud-only AI is a non-starter
Every vendor demo assumes your data moves to their cloud. Your data-residency rules say no, so the conversation ends before it starts.
The ERP can’t move
Decades of history live in an on-prem SAP system nobody is ripping out. Any AI that can’t reach it reads a fraction of the business.
Vendors demo on clean SaaS data
The demo runs on a tidy cloud dataset. Your reality is an on-prem ERP, file shares, and spreadsheets — where the demo never visits.
The digging stays manual
A quote means digging through hundreds of thousands of lines — permutations, discounts, what this customer paid last year. Days of it, by hand.
The skepticism is earned
After years of SaaS pitches, the bar is simple: show it running against our data, in our environment, or don’t ask for the meeting.
Every quarter the AI question stays open, the manual work wins.
What the manual way looks like while deployment stays unresolved. Your numbers will differ — the first engagement puts figures on yours before anything gets built.
From an anonymized engagement — a transformation lead at a European manufacturer running SAP on-prem
Deploy in your environment. Connect to what’s there.
No migration, no new system of record. The AI comes to the data — your servers or your cloud tenant, alongside the systems you already run.
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Map the work where it happens
We trace one workflow end to end — the quoting desk, the order intake — and find where the hours go before anything gets built.
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Deploy inside your perimeter
AI agents install in your environment — your Azure tenant or your on-prem servers — connected to the ERP, file shares, and mailboxes already there.
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Agents run it, people keep the judgment
Each agent executes against your encoded rules; anything that breaks one lands in an exception queue for a person. You own the IP outright.
SaaS AI asks your data to move. On-prem AI comes to the data.
Every cloud AI tool prices the same hidden cost into the demo: moving your data into their world. For a company with residency rules and an ERP that can’t move, that cost is the whole deal.
We build AI agents that run where your data already lives — your cloud tenant or your on-prem servers — reading the ERP and file shares in place. Your data never leaves; you own the IP.
SAP is on-premise, it is not in the cloud — can we host an AI here, probably a hybrid system.
Led by Peter Enestrom.
Asked by transformation and IT leads.
The straight answers, before you book anything.
On-premise AI is AI that runs inside infrastructure you control — your own servers, your private cloud tenant — instead of a vendor’s SaaS platform. The models and agents execute where the data already lives: nothing is copied to a third-party cloud, and data-residency rules are met by construction. In practice, most deployments are hybrid — on-prem AI for the systems that can’t move, like an on-prem ERP, connected to a private cloud tenant for compute. What defines on-premise AI is not where the chips sit but who controls the perimeter: your environment, your security policies, your data. A person still reviews the exceptions the rules reject — on-premise describes where the AI runs, not whether anyone is watching. For a company running SAP on-prem with decades of history it can’t relocate, on-premise AI is the difference between using AI and watching a demo of it.
Yes — that is the normal case. The agents connect to what is already there: the ERP database and application layer, file shares, mailboxes. Modern ERPs, including on-prem SAP S/4, expose the interfaces agents read and write through. Where an interface does not exist, agents work at the document level — reading exports and PDFs the way a person would. Nothing needs to move to the cloud first.
Not necessarily. Most deployments are hybrid: data and connections stay in your environment while model compute runs in your private cloud tenant — your Azure, under your agreement. Where compute must stay on-prem too, open-weight models you choose and can name run on modest hardware. The architecture follows your residency rules, not the reverse.
You do. The agents, the encoded rules, and the documentation are your IP, deployed in your environment from day one. There is no license to renew and no dependency on our infrastructure — support stays available, but the system is yours.
It starts with one workflow, inside your perimeter, against your real data — the quoting desk is a common first. You watch the agents run against your ERP before anything scales. Skepticism is the right posture; the proof is a working system under your roof, not a demo on clean SaaS data.
No — the agents meet the systems where they are. If an ERP move is on your roadmap, AI built in your environment comes with you; our ERP-migration insight covers how the two fit together. Waiting for a clean future state is how the manual work wins.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.