RESTART provides managed server, GPU and cloud resources for running enterprise AI platforms, RAG systems, AI assistants and private AI environments. We combine computing power, architecture, implementation, information security and support into a single service for enterprise customers.
Enterprise AI solutions require not only the application and model, but also a properly designed infrastructure: GPU resources, fast disks, secure environments, backup, network connectivity, logging and monitoring. This is especially important if the system works with internal documents, personal data, employee requests, contracts, tenders or regulations.
RESTART helps the customer quickly deploy an AI environment without independently searching for individual suppliers, servers, GPUs and DevOps teams. We take on the architecture, launch and maintenance of infrastructure for a specific AI scenario, and for the client it looks like a single managed environment under the RESTART brand.
Quickly deploy a testbed for demonstration, MVP, knowledge base testing and early adopters.
Prepare a production environment for production users, API integrations, AI chat and service processes.
Allocate resources for local models, embeddings, reranking, OCR and processing of document arrays.
Deploy private AI taking into account the requirements for data, access, logs, personal data, trade secrets and information security.
Transparently account for compute resource consumption in pilots, implementations, and maintenance.
This is not a VPS directory or an abstract server rental. RESTART provides a managed AI infrastructure related to the implementation of the platform, RAG, AI agents, integrations, security and industrial operation.
| Component | What does it include |
|---|---|
| Computing power | CPU, RAM, SSD/NVMe, GPU resources, network, public and private addresses if necessary. |
| AI servers | Outlines for LLM, embeddings, RAG, document processing, reranking, OCR and AI agents. |
| Production / Test / Development | Separate environments for production, testing, acceptance, demonstration and development. |
| Containerization | Docker / Docker Compose, preparation for Kubernetes architecture during project development. |
| Storage | PostgreSQL, vector storage, file or S3-compatible storage, backup environment. |
| Safety | Access control, secrets, logs, basic protection against leaks and prompt injection, information security regulations. |
| Monitoring | Health checks, logs, availability control, resource consumption control and scaling recommendations. |
| Escort | Support, updates, scaling, operational advice and communication with the project team. |
For an enterprise project, it is important to separate experiments from acceptance and industrial operation. Therefore, AI Compute is designed as a set of managed environments, where each environment has its own resources, access rights, data, regulations and level of control.
| environment | Purpose | Example resources | What we control |
|---|---|---|---|
| Development | Development, assembly, debugging, experiments of the RESTART team or project team. | 4-8 vCPU, 16-32 GB RAM, 150-300 GB SSD, GPU as needed. | Developer access, sandbox data, test pipelines, secrets and logs. |
| Test / Staging | Verification of releases, integrations, demos, UAT and load tests. | 8+ vCPU, 32+ GB RAM, 300+ GB SSD/NVMe, L4 / A10 / L40S or equivalent for the task. | Test base, vector storage, update regulations and acceptance scripts. |
| Production | User experience, production API, AI chat, RAG, Service Desk AI and corporate knowledge bases. | 16+ vCPU, 64-128+ GB RAM, 1+ TB NVMe, L40S 48 GB / A100 80 GB or equivalent as agreed. | Availability, backup, monitoring, logs, limited admin access and SLA. |
| Backup / Storage | Storing backups, source files, documents, build artifacts, and logs. | A separate storage environment with regulations for storing and unloading data. | Storage periods, access rights, recovery and procedure for deleting data after the project. |
A typical outline includes a user interface, backend API, database, vector storage, file storage, task queue, workers, LLM adapters, a GPU node for models, and a separate monitoring layer. For enterprise projects, the contours of development, testing and production are separated.
The specific configuration is fixed in the technical specifications: placement, access model, composition of environments, backup, SLA, information security requirements and the procedure for uploading or deleting data after the completion of the project.
Restart AI Enterprise Platform — RESTART platform for corporate use of AI: knowledge bases, RAG, AI agents, Service Desk AI, document management, logging and integration with corporate systems. For stable operation of the platform, an infrastructure designed for document processing, vector search, user requests, LLM providers and integrations is required.
The AI infrastructure solves this problem: the customer receives not only a software product, but also a ready-made computing environment for running it. This speeds up the transition from pilot to production without searching for separate contractors for servers, DevOps and information security.
The format is selected according to the stage of the project: from a quick pilot to a closed production environment. You can start with a small bench, then expand resources, add GPUs, separate environments and enable SLAs.
For the development of AI modules, experiments with RAG, backend/frontend assembly and demonstrations within the project team.
Dev server, base CPU/RAM/SSD, Docker environment, developer access, logs and temporary GPU connection.To pilot RESTART AI Enterprise Platform, UAT, document testing, demos and integration testing.
Test/staging server, GPU if necessary, test database, vector storage, accounts and update regulations.For work users, AI chat, Service Desk AI, enterprise knowledge base and API integrations.
Production server or group of servers, GPU, PostgreSQL, vector DB, file storage, backup, monitoring, SLA and support.For closed enterprise landscapes, private cloud, on-prem model and scenarios with increased data requirements.
Isolated environment, data transfer restrictions, logs, secrets, information security regulations and integration with customer policies.Comprehensive package: RESTART AI Enterprise Platform, infrastructure, implementation, setting up AI agents and support.
Platform, dev/test/prod environments, document uploads, integrations, administrator training and usage reporting.In projects for implementing AI solutions, RESTART can record the consumption of computing resources in machine-hour format. This approach is convenient for pilots, pilot production, and projects where the workload changes as documents are loaded, users connect, and the number of AI scenarios grows.
The reporting can reflect the period of use of the infrastructure, the composition of servers and environments, the amount of capacity provided, GPU resources, storage, backup, technical support, maintenance work, incidents, availability and scaling recommendations.
The cost is calculated individually and depends on the composition of the environments, GPU resources, storage volume, requirements for availability, backup, security and maintenance. For pilot and project work, an hourly accounting model for computing resources can be used.
The infrastructure is designed for real AI scenarios: RAG, AI agents, Service Desk, document processing and integrations.
RESTART is responsible for connecting the application, data, models, contours and maintenance.
The project can include audit, threat model, ISPDn, CII, DevSecOps and protection of AI environments.
The architecture is evolving from MVP and pilot to production, multi-tenant, private cloud and on-prem delivery.
AI Compute strengthens the RESTART product line: computing power becomes part of a managed AI environment, rather than a separate purchase of servers. On such an infrastructure you can run a platform, RAG, service desk, AI for contracts, tenders, development, information security, HR, industry packages and application projects like 1trAIner and social AI services Spina Bifida.
Industrial R&D pilots may require a separate computing loop: processing time series, signals, telemetry, images, documents, RAGs for engineering materials and securely running AI assistants. AI Compute RESTART can be used as an infrastructure base for such scenarios after assessing the data, security and operational requirements.
No. RESTART provides computing power as part of a managed AI infrastructure: with architecture, configuration, maintenance, monitoring and connection with the AI platform and information security requirements.
Yes, if the customer needs an AI loop for corporate RAG, LLM integrations, AI assistants, document processing or model testing. But the maximum value is achieved in conjunction with RESTART products.
Yes. The architecture can be adapted to a private cloud or on-prem model. Requirements for placement, access and processing of data are fixed in the contract and technical specifications.
No. The GPU is needed for local LLM, embeddings, reranking, processing large arrays of documents and high-load scenarios. For some scenarios, a CPU environment or external LLM providers according to an agreed architecture are sufficient.
The project includes access control, secret management, logging, backup, restrictions on data transfer to external providers and additional information security measures according to customer requirements.
Yes. For a pilot, a test/staging loop, a demo dataset, a limited number of users, and an agreed upon set of AI scenarios are usually sufficient.
Describe the task, current systems, constraints, and expected results. We will offer a practical first step: diagnostics, pilot, audit, roadmap or project team.
