Phaeonics installs and operates open AI models on company infrastructure: behind the company firewall, connected to the company's own knowledge. Sign-in runs through the existing directory.
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What a team types into an AI window is costings, client names, source code, contract drafts. With cloud services those inputs leave the house and sit under the provider's terms — which change with every pricing round. Local models turn the direction around: the model comes to the data.
Inputs, answers and documents stay on the company network. There is no third party to trust, and none to audit.
Cost is tied to the machine, not to usage: hardware once, then electricity. Asking more does not cost more.
Open model weights still run tomorrow: no outside price rise, no discontinuation, no forced API migration.
Six building blocks, bookable separately, together a running system. Each at a fixed price agreed up front.
We evaluate open models against the actual tasks — with documented, reproducible criteria and a measured run on real examples.
From a single GPU server to a cluster: sized for the expected load, with a cost frame before anything is ordered.
An inference server on the company network, access via browser and API, connections to DMS, databases and wikis. Every answer cites its source.
Sign-in through the existing directory, rights per department, logging agreed with IT, data protection and the works council.
A pilot with a defined user group and metrics agreed beforehand; training on the teams' real tasks.
Updates, monitoring and model changes in production — by us under contract or, fully documented, by internal IT.
The reference architecture in one picture: clients talk to the inference server through a gateway; the server pulls context from the knowledge index, fed by the systems already in place. No line leads outside.
Every component runs on company infrastructure — in the company's own server room or in a private EU cloud.
Review the use cases, check the data situation, calculate the hardware. Result: a plan with costs and expected benefit, ordered by what pays off first.
A defined user group works on real tasks; the metrics are agreed beforehand. At the end there is a number.
SSO, roles, team training, further sources connected — in waves, department by department.
Monitoring, updates, a model change when better open weights appear. By us under contract, or by internal IT.
Processing on company infrastructure: no third-country transfer, no additional processor at the core. Where we operate the system, a DPA covers the details.
We document models, data flows and intended uses so that classification and the duties that follow can be determined.
Logging and evaluation are configurable and disclosed — a basis that IT, data protection and the works council can work with.
Phaeonics belongs to a group that builds its own simulation and AI systems and keeps them in continuous operation. Evaluating models, operating them, connecting them to knowledge bases: our daily work, with the same tools offered here. Phaeonics GmbH, HRB 114306, Amtsgericht Düsseldorf.
30 minutes with someone who builds these systems — not with a sales team. Afterwards it is clear whether local AI pays off and what a pilot would cost.
Or directly by mail: connect@phaeonics.io