Private AI infrastructure. Built around your business. On-premises models, knowledge and workflows inside a defined environment.
NEUROSERVERWe define users, data, models and operating needs first. Hardware, runtime, knowledge, access and handover then form a coherent deployment.
01Hardwarestation · GPU · server
02Models & runtimeLLM · runtime
03Knowledge / RAGsources · index
04Access & applicationspermissions · APIs
05 / HANDOVERVerify & hand overChoose a station or project-specific GPU system around model size, memory, concurrent use and space. The team gets a scoped platform.
Prepare compatible models, drivers and runtime; record the tested configuration so the team knows what is installed.
Prepare approved sources, index and retrieval with refresh and access rules. Users get source-linked answers to evaluate.
Scope interfaces and connectors, separating read access, permitted actions and approval points.
Test agreed tasks, record limits and give the team configuration, documentation and an operating plan.
A working private AI environment with equipment, software, knowledge and responsibilities agreed together.
A compact local AI workstation reference for models, private RAG, development and a defined team pilot.
Unified memory is a shared pool, not 128 GB RAM plus another 128 GB of dedicated VRAM. Memory available to a model depends on system and runtime allocation.
Final enclosure, components, software scope and delivery terms are confirmed in the proposal.
Each scope item is agreed for the actual project.
HardwarePlatform matched to use.
RuntimeDrivers and dependencies.
ModelsSelected, tested profile.
KnowledgeApproved sources and retrieval.
AccessInterfaces and permissions.
HandoverEvidence and operating plan.
Private RAG retrieves relevant passages from approved knowledge, provides them to a local model as context and links the answer back to a source.
Source documents, index and retrieval can remain within your managed infrastructure. Access must follow each source’s permissions.
External APIs and update services require a deliberate data path, scoped permissions and an explicit decision about what can leave the environment.
DOCUMENTS / KNOWLEDGE / SOURCESRAG does not train model weights on every document or guarantee a correct answer. Evaluate retrieval, missing evidence and difficult questions.
Send the project owner your project name, intended use and requested access level. The owner approves the scope before access is granted.
Example source passage“How do I request access to the project archive?”
Contact the project owner with the project name, intended use and access level you need. Access follows approval of that scope. [1] Read source
The supporting passage is shown here. This editorial example is not a connected knowledge base or a live AI response.
Integrations need bounded permissions, with reading and writing treated separately. Agree owners for updates, logs, backups, retention and support. Offline requirements need their own architecture and test.
Choose a task the team already understands. Define representative inputs, useful output and evidence for the pilot.
Help employees find relevant information in manuals, policies, technical references and project files. A retrieval-based assistant can use selected passages to draft an answer and point back to the source.
Summarise reports, compare selected documents or extract information into a defined format. OCR and parsing can be included for scans.
Explore local coding assistance, repository questions and technical documentation. Match models and tools to your team’s languages and repositories.
Explore local speech-to-text, transcript review and summaries for agreed audio sources. Voice interfaces and text-to-speech are separate components to qualify.
Evaluate image generation and visual document workflows where suitable models and software support the selected hardware.
Collect evidence from approved material and turn it into a reviewable draft. Web-connected research introduces a separate external data path.
Bring an assistant into a CRM, ERP, portal or support workflow where suitable APIs and permissions exist. Define read, write and approval boundaries.
Model, runtime and application compatibility are checked on the selected platform. Review requirements are part of each pilot.
The software layer helps put hardware, models, runtimes and operating profiles into a clear working context.
Keep a requested setting, applied configuration, observed value and verified result distinct. Availability depends on the hardware and release; ask for a demonstration of the proposed configuration.
Ask for a current demonstrationThis diagram explains the roles of the control layer. It is not live telemetry or proof of a shipped function.
Start with a compact station, larger enclosure or knowledge storage. GPU workstations and servers are project-specific expansion paths.
For local model exploration and a defined team pilot.
Discuss this configuration ↗
A larger workstation format where chassis, power and I/O shape the configuration.
Discuss this configuration ↗
Storage-oriented architecture for document collections, indexing and local knowledge workflows.
Discuss this configuration ↗Images and configurations are references. Final enclosure, components, ports and software scope are confirmed in the proposal.
Local models and a first working use case.
Configure chassis, power and interfaces around the task.
Scope sources, index and knowledge storage.
Dedicated GPU workstations and servers for measured demands.
Expand around validated needs; nodes do not automatically pool memory.
Hardware, software, knowledge and a useful task are scoped together. The team gets a tested configuration and an operating plan.
Agree the evidence that will justify deployment, then work through a defined sequence.
Users, source material and representative work. Output: workload brief and acceptance criteria.
Data, access, infrastructure and software dependencies. Output: hardware and software scope.
Prepare the environment and only the sources and actions in scope. Output: testable configuration.
Answer quality, latency, concurrent use and limits. Output: evidence and deployment decision.
Document configuration, owners and support. Output: operating plan and next expansion step.
The practical questions behind a private AI deployment.
It is an AI environment whose hardware, model execution, data access and operating rules are controlled for your organisation. It can include a local workstation, a server, internal knowledge retrieval and integrations. The actual privacy boundary comes from the architecture and configuration, not the name of the product.
No. On-premises describes where the system runs. Initial model downloads, updates, external connectors or cloud services may still require a connection. An offline or air-gapped environment needs those dependencies to be reviewed and an agreed update process tested separately.
Compatibility depends on the model architecture, format, quantization, memory needs, context length, runtime and hardware support. We select candidates for the task and validate them on the intended configuration. A model fitting into memory is not a guarantee of a useful response time.
No. Unified memory is shared by the system, including the CPU and integrated graphics. The operating system, runtime and other processes use part of it. Model weights, context and runtime allocations must fit within the available budget. See the reference configuration and memory explanation.
A private RAG workflow can be scoped around approved documents and knowledge sources. Preparation includes parsing, indexing, refresh rules and access control. Supported formats and connectors are confirmed with representative files; scans and complex tables may need additional processing.
Not necessarily. RAG retrieves passages from your collection and includes them as context for a request, without retraining the model for each document. Fine-tuning serves a different purpose, such as adapting behaviour or output format. The task determines whether it is useful.
No. It can improve access to relevant source material and make references visible, but a model can still omit, misinterpret or invent information. Evaluation should include difficult questions, missing evidence and access restrictions, with human review where consequences require it.
A shared environment can be designed, but useful capacity depends on simultaneous requests, model size, context and response-time expectations. The pilot should test the expected usage pattern. A user count alone is not enough to size the hardware.
Where suitable APIs and technical access are available, integrations can be included in the project. The scope defines what is read, which actions are allowed, how failures are handled and when a person must approve a change. The model should not receive unrestricted system access.
The proposal sets out the hardware revision, operating system and licences, selected models, software configuration, deployment work and documentation. Knowledge preparation, connectors, training and support are explicit scope items. Control Center functions depend on the delivered release and hardware.
The proposal confirms price, delivery, warranty, implementation and any ongoing support separately, so both the initial cost and the operating responsibilities are clear. The terms are specific to your configuration and project.
Describe one priority task, the people who will use the result, the type and approximate volume of data, and any current infrastructure or connection limits. Non-sensitive examples are enough to start. Do not send confidential files before agreeing how they will be handled.
Tell us the task, who needs the result and the kind of data involved. We can discuss a useful starting point and what should be checked in a pilot.
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