Data under control
Documents, contracts and knowledge bases are not sent to an external model provider and are not used to train third-party systems. This makes it easier to meet GDPR requirements and protect trade secrets.
← Server infrastructure, HPC and AI
Private AI by PCS
AI close to your data. Compute power under your control.
We design and deploy private AI workstations and GPU servers with language models, a company knowledge base and access control. Everything runs on your network — without sending documents to external services.
PCS PRIVATE AIData · GPU · Control
AI in your environment
Not just GPU power.
A ready‑to‑use environment
for working with models, data and users.
Private AI is part of an organization’s infrastructure: it needs properly sized compute resources, data, networking, access control, backup and administration. PCS designs these elements as a single whole. We make sure that from day one the workstation works with your network, your Active Directory domain and your backup system — as part of your infrastructure, not an add-on sitting next to it.
You get more than a machine — you get a ready-to-use environment: selected models, a chat interface for employees, connected document sources, sign-in with company accounts, backup and monitoring. Every configuration undergoes 72-hour stress testing before delivery, and quiet configurations (< 40 dB(A)) let you place the workstation in the office — no separate server room required.
Why local
The model should work for the organization — not the other way around.
A local environment gives you room to use AI responsibly where data, cost and performance really matter.
Predictable cost
A one-time hardware investment instead of per-seat or per-query fees. Cost doesn’t grow with usage — the more your team works with AI, the lower the cost per query.
Performance without surprises
Response times don’t depend on the load on someone else’s cloud or on API limits. The environment runs on your network — even when your internet connection is down.
Integration with everyday work
Permissions inherited from Active Directory mean the assistant sees only the documents a given employee has access to. AI uses company data and systems without bypassing security policies.
Configurations
From a specialist’s workstation to a shared AI server.
We match the configuration to the type of models, data volume, number of users and your expected growth path.
AI workstation
For a specialist or small team that wants to work with a local language model, data analysis, images and prototypes.
- GPU workstation or a compact DGX Spark
- local data and models
- upgrade path to a team workstation
Typically: 1 GPU (e.g., RTX PRO with 96 GB of VRAM) or DGX Spark, models up to approx. 70 billion parameters, 1–5 users.
Shared team workstation
For departments that need a shared environment for working with models, documents and compute jobs.
- multi-user access
- centralized data and permissions
- network and storage integration
Typically: 2–5 GPUs, from a dozen to several dozen concurrent users, a shared departmental knowledge base. The 5-GPU configuration can run larger models.
GPU server and AI environment
For larger models, multiple processes and developing your own AI solutions within your company infrastructure.
- scalable GPU resources
- high-performance networking and storage
- administration, backup and monitoring
Typically: multi-GPU servers and DGX-class nodes, the largest models, many services running in parallel, API integrations with company systems.
What goes into the environment
Every component has to support a specific AI task.
The GPU matters, but only as part of a well-matched system: with memory, data, networking, power and a runtime environment.
PCS PRIVATE AIGPU
COMPUTEFrom 1 GPU to DGX nodes
COMPUTEFrom 1 GPU to DGX nodes
Software layer
Open models and proven tools, running on your network.
We build the environment on open models and software, with no lock-in to a single vendor. You can swap a model or add another one when a better one for the task comes along.
Models
Model runtimes
User access
Company knowledge (RAG)
Use cases
AI that supports specific tasks.
We don’t build models for the sake of technology. The starting point is a process that can be improved and data that can be used safely.
Employees ask questions about procedures, contracts and technical documentation, and the assistant answers based on company resources — citing the source document so the answer can be quickly verified.
Recordings of meetings, calls and consultations are turned into text and concise summaries — entirely on-premises. Important wherever recordings must not leave the organization: in public administration, law firms and HR departments.
Incoming documents, tickets and messages are categorized automatically, and the relevant information is extracted and passed on — instead of being retyped by hand.
Image and video analysis, quality control and work with technical data — without sending recordings or photos outside the company network.
A safe place to test models, prototypes and integrations, plus a local coding assistant that works with your code without sharing it externally.
The same workstation can handle CAD/BIM design and GPU rendering while also working as a local AI node — one investment, two uses.
CAD/rendering workstations →Security and compliance
We design private AI the same way as the rest of your critical infrastructure.
A local model is only the beginning. Security comes down to permissions, activity logging and where the data goes — which is why we design for them from day one.
Local processing makes it easier to meet GDPR requirements and internal security policies.
See our security architecture →AI as part of your infrastructure
An AI workstation shouldn’t be an island.
Security, data and user access need to be designed together with the organization’s entire IT architecture.
IT architectureNetworking, routing, servers, storage and virtualization as the foundation of the environment.
CybersecuritySegmentation, permissions, endpoint protection and data access control.
Backup and continuityLocal and remote backups, retention and a recovery plan for critical data.
IT outsourcingMonitoring, administration, updates and ongoing development of the environment after deployment.
The PCS process
We start with the task, then size the compute.
That’s how we avoid a costly configuration that looks good on a spec sheet but doesn’t fit the way you actually work.
- 01Goals and data
We define the use case, data sources, number of users, and security and compliance requirements — before a single graphics card is named.
- 02Environment sizing
We match the model to the task first, and only then select the GPU, VRAM and RAM, storage, networking and how users will access the system.
- 03Build and integration
We assemble the configuration and test it for 72 hours under full load, install models and tools, and connect document sources and company-account sign-in.
- 04Development
We monitor usage, update models and software, expand resources and plan further AI use cases.
AI deployment consultationLet’s find out which AI tasks are worth running locally.
Let’s talk about a document assistant, meeting transcription, data analysis, image and video, a team environment or an AI lab — we’ll size the compute and configuration to your data and budget.
- GPU sizing without subscriptions
- Work on your own local data
- Talk to an engineer, not a salesperson
Confidential needs assessment
Response within 24 hours on business days