Private AI studio · Frankfurt, DE
We build and fine-tune local language models on dedicated NVIDIA DGX infrastructure—so your confidential knowledge creates value without leaving your control.
THE MOST VALUABLE AI ISN'T THE ONE THAT KNOWS EVERYTHING.
IT'S THE ONE THAT KNOWS YOUR BUSINESS.
01 / Solutions
From a two-week feasibility check to a production-ready private model. Every engagement has a defined outcome, fixed starting price, and a clean handover.
Clarity sprint
Find the highest-value use case in your business and determine whether local AI is the right fit.
Working prototype
A working private assistant trained on a focused slice of your knowledge, deployed for real users.
Production system
Your own specialized model and inference stack, tuned around your workflows and operating boundary.
All prices exclude VAT. Final scope follows a technical review. No data transfer is part of ordering.
02 / The process
One accountable technical partner from first question to handover. No black-box platform dependency.
We isolate one valuable workflow and agree what success looks like before touching infrastructure.
Your source material is cleaned, permissioned, and shaped into a dependable training or retrieval set.
We adapt and evaluate an open model on dedicated DGX compute within the agreed data boundary.
You receive the working system, operating documentation, and a clear path to run it independently.
03 / Our principles
Confidential AI is primarily an ownership question: who controls the data, the model, and the machines running it? We make that boundary explicit before we build.
Discuss your requirements ↗No training global models. No resale. No unnecessary retention. Data handling follows an agreed scope.
Build and inference can run in Germany, in your environment, or across a deliberately designed hybrid.
We favor portable open-weight models and documented systems that do not lock your business to us.
Useful answers are not enough. We test quality, refusal behavior, leakage, latency, and operating cost.
04 / Example engagement
An engineering team has years of service manuals, resolved tickets, and specialist notes. Finding one dependable answer means searching several systems—or interrupting the same senior colleague again.
DG AXIOM prepares the approved documents, connects them to a compact open model, and makes every answer cite its source. The pilot runs locally with a small group before broader access is considered.
The agreed test is practical: can staff resolve a defined set of questions faster, with correct source references and without exposing the underlying material beyond its permission boundary?
05 / The lab
DG AXIOM is built around dedicated NVIDIA DGX Spark systems—a compact environment for prototyping, fine-tuning, and validating private AI workloads without sending your core knowledge to a public model.
06 / Common questions
Not necessarily. The correct deployment boundary depends on your risk, systems, and use case. A pilot can be designed for your environment, dedicated German infrastructure, or a deliberate hybrid.
No. For knowledge that changes often, retrieval-augmented generation may be simpler and easier to verify. Fine-tuning is useful when the model must learn stable domain language, behavior, or format.
No technology vendor should make that blanket promise. We design for data minimization, explicit processing boundaries, and documented controls; your legal basis and organizational compliance remain a joint review with your data-protection experts.
The goal is the opposite. We prefer portable open-weight models, standard deployment components, and a documented handover so your internal team or another qualified partner can operate the system.
We discuss one workflow, what data it depends on, and what would make it valuable. Please do not send confidential material through this website. A secure exchange is established only if the project moves forward.
Have a use case in mind?
No pitch deck marathon. Bring one workflow, one data concern, and 30 minutes.