AI Box for business
Local artificial intelligence inside your company. Ready AI systems with GPUs, local models, a corporate knowledge base and a software contour.
From your first local AI to an enterprise contour
We do not sell a GPU server. We assemble a ready infrastructure for a specific AI workload: models, users, data, security and further evolution.
AI Box Start
Local AI for a small team and a first corporate AI project.
- GPU24–32 GB class
- RAM64–128 GB
- Storage2–4 TB NVMe
- AILLM / RAG
- Users5–15
AI Box Business
Production AI server for enterprise knowledge, RAG and AI assistants.
- GPU48–96 GB aggregate
- RAM128–256 GB ECC
- Storage4–8 TB NVMe
- Network10 GbE
- Users15–50+
AI Box Enterprise
Private AI contour for multiple departments, larger models and production inference.
- GPUMulti-GPU
- VRAM192 GB+
- RAM512 GB–1 TB+
- RuntimeMulti-model
- HAAs required
Actual user count depends on the model, context size, nature of queries, latency requirements and concurrent load. Configuration is chosen to match the task.
We choose hardware from the task
The question is not which GPU is trendier right now. It is which model you intend to run, how many people will work with it, what data it needs to understand and what actions it should perform. AI Box is designed from the workload, not the other way around.
Which AI Box do you need?
Choose a task — we will show a typical architecture. The final configuration is defined after analysing the model, the data and the workload.
Choose a scenario
This is not a price calculator. It is a starting point for architecture.
AI Box Business
Corporate AI server for an internal assistant, document search and work with the corporate knowledge base.
Find exactly the right AI infrastructure
Individual solutions for specific models, scenarios and hardware classes. AI Box remains the single entry point — we will discuss the task and select a configuration.
AI Box on RTX 5090
Compact AI workstation for development, local LLMs, RAG and first production scenarios.
Discuss →AI Server on RTX PRO
Professional GPU infrastructure for enterprise inference and operational AI workloads.
Discuss →GPU Server on L40S
Server-class configuration for production AI, inference, RAG and multiple users.
Discuss →Enterprise AI Server
Multi-GPU infrastructure for large models, intensive inference and scalable AI systems.
Discuss →Server for a local LLM
Selection of GPU, memory and inference runtime for a specific local model and user count.
Discuss →Server for enterprise RAG
AI infrastructure for search and work with enterprise documents and knowledge bases.
Discuss →Server for Ollama
Local inference stack for quickly running open models and enterprise AI applications.
Discuss →Server for Qwen
Configurations for Qwen and other open-weight models, considering size, context and concurrency.
Discuss →Server for DeepSeek
Local infrastructure for inference and enterprise use of DeepSeek-class models.
Discuss →AI Server for agents
Infrastructure for AI agents, tools, APIs and integration with enterprise systems.
Discuss →AI Server for Computer Vision
GPU configurations for video analytics, image processing and specialised models.
Discuss →Private AI Infrastructure
A full private AI contour: compute, models, runtime, knowledge, security and integrations.
Discuss →AI Box is only the beginning
A server gives AI compute power. But enterprise intelligence only emerges when compute connects to models, knowledge, enterprise structure and its systems. That is why we design not only hardware, but the whole intelligence contour.
The server is a box. The model is the engine. Knowledge is memory. Ontology is the structure of understanding.
Agents become participants in the business only when all of this is connected to the reality of the enterprise: its people, objects, processes, rules and systems.
What can run on your AI Box
We choose the model and runtime according to the task. AI Box can become the foundation of local inference, enterprise RAG or a full AI application.
Qwen
Open-weight models for local inference, enterprise assistants and reasoning tasks.
DeepSeek
Local execution of suitable models with the right GPU configuration.
Llama
A broad ecosystem of open models and tools for enterprise AI.
Mistral
Open-model ecosystem for local inference and specialised tasks.
gpt-oss
Open-weight models for scenarios that require controlled infrastructure.
Ollama / vLLM
Inference runtime and API layer for running and integrating local models.
Why does a company need local AI?
When AI works with the internal knowledge of the enterprise, the infrastructure question becomes a question of control.
Control of data
Corporate documents, contracts, technical information and other data can be processed inside a controlled perimeter.
Independence from external APIs
The company independently manages infrastructure, models, access, logging and updates.
Predictable infrastructure
You invest in your own compute resource and can build multiple AI scenarios on top of it.
Integration with the business
AI can be connected to ERP, CRM, ECM, 1С, file storage, databases and internal APIs.
AI can operate inside your perimeter
We do not promise an abstract “zero leakage”. We design a controllable architecture in which it is clear where data, models, logs and access points are.
Not sure which AI Box you need?
We start not with a GPU, but with your task. We determine the model, the workload, the data and architecture requirements — then we size the infrastructure.
Indicative assessment cost: from ~90k ₽ depending on scope.
How AI enters the enterprise
Private Enterprise Intelligence — more than a local server
Why the next enterprise infrastructure is not just a compute cluster, but a private intelligence layer that connects models, knowledge, ontology, people and business systems.
Frequent questions about AI Box
What is an AI Box?+
An AI Box is a ready compute system for running local AI. Unlike an ordinary GPU server, we treat the AI Box as the foundation of a full AI contour: GPU, models, inference runtime, RAG, knowledge, security and integrations.
Can an LLM run fully locally?+
Yes, suitable open-weight models can run on infrastructure inside the enterprise. The specific configuration depends on model size, quantisation, context, user count and required speed.
What server is needed for a local LLM?+
There is no universal answer. For a small model and a few users, a single GPU may suffice. Larger models and production workloads require professional GPUs and sometimes multi-GPU architecture.
Can we use Ollama?+
Yes. Ollama can be used as one of the local model runtime options. For production scenarios, specialised inference stacks including vLLM and others are also considered.
Can we use Qwen, DeepSeek or Llama?+
Yes, provided the specific model, licence, hardware configuration and chosen inference runtime are compatible. We size the infrastructure for the specific model and task.
What is enterprise RAG?+
RAG allows AI to retrieve relevant information from enterprise sources at the moment of generating an answer. This allows internal company knowledge to be used without embedding all of it into the model parameters.
Is fine-tuning required?+
Not always. We first determine whether the task can be solved through retrieval and enterprise knowledge, prompt engineering, tools and agents. Fine-tuning is applied where it genuinely adds a required specialised capability.
Can AI be connected to 1C, ERP or CRM?+
Yes. Private AI can be connected to enterprise systems via APIs, databases, connectors and specialised tools. The architecture depends on the specific system and security requirements.
Is AI Box a final product?+
For a small task, it may be. For a large enterprise, AI Box usually becomes the first level of Private Enterprise Intelligence: the compute foundation on top of which knowledge, ontology, agents and integrations are built.
Can we start small?+
Yes. We deliberately build the architecture so an enterprise can begin with a pilot, verify economic effect, and then scale infrastructure and the intelligence layer.
Tell us what your AI should do
We will determine the right model, infrastructure, AI Box and next step — from a first pilot to Private Enterprise Intelligence.