What’s in the direction
A sovereign company GPU contour
We assemble and install server racks — from procuring cards to physical install in your cabinet. An independent AI foundation from scratch, without cloud providers.
Key usage scenarios
GPU INFRA
Your own AI infrastructure
We procure hardware, install, test, and set up the environment inside the company.
- Selection and procurement of GPU, servers, racks, and network
- Physical install in your cabinet / server room
- Environment setup: drivers, Docker/Kubernetes, monitoring
- Stress tests and infrastructure acceptance on site
LLM & RAG
AI agents: RAG, fine-tune, and knowledge
Corporate agents on internal knowledge bases. Local inference or cloud — by data and load requirements.
- RAG over company policies, contracts, and wiki
- Training and fine-tune on internal data
- Support, HR, and analytics AI agents
- Local / cloud / hybrid — for the job
AI INTEGRATION
AI agents and AI integration into the business
Agents, RAG, bots, and automation — we embed AI into your processes, CRM, and product.
- AI agents on the website, in messengers, and in internal systems
- RAG over your documentation and knowledge base
- Bots and automation of sales / support funnels
- Link to CRM, ERP, and custom services
SALES AI
AI agents for sales and CRM
Lead qualification, 24/7 answers, CRM write-back. ChatNeuron or a custom pilot.
- Website widget and messengers
- Qualification and handoff to a manager
- amoCRM / Bitrix24
- Pilot metrics in 2–4 weeks
Cases for this direction
Cases: AI agents & infra
FAVORIT / PinMaster
PinMaster — Pinterest automation prototype
PinMaster — desktop Pinterest automation prototype for FAVORIT. Worked locally; hosting and scale exceeded budget — project unfinished.
Project (NDA)
AI avatar for streams
NDA: AI broadcast with stream — live face swap and character select instead of the camera face. Demo on the case page.
FAVORIT
FAVORIT · Stage 2: SaaS product card generation for marketplaces
FAVORIT stage 2: full web service for info cards — realtime generation, premium UI, info-card and model modes.
FAVORIT
Costbl — SaaS cost estimation for metal parts from drawings
Costbl: SaaS cost estimation from drawings. 5+ months, 2 stages — from an AI chat to a multi-agent graph. Video of a real calculation.
B2C NDA
SportsAge AI — AI tips for football matches
SportsAge AI: AI tips for football matches. Client ~200,000 ₽/mo for over six months. Product screenshots on sportsageai.ru.
B2C NDA
AI Tutor: smart learning for kids
AI tutor MVP for kids: homework, progress, question bank. Project no longer developed.
FAVORIT
FAVORIT · Stage 1: SaaS marketplace card generation (Telegram bot)
FAVORIT stage 1: Telegram bot for product card generation (v1). Weak-design prototype. Continued as web v2.
MONSUROVICH
MONSUROVICH
MONSUROVICH VK bot with AI: roll order → kitchen and POS. Chain of 3 delivery points.
Technology stack and security
GPU: NVIDIA RTX 6000 line
Professional flagship GPUs for workstations, servers, and AI jobs. Generations on Ada Lovelace (RTX 6000 Ada) and newer Blackwell-based cards.
- Use: rendering, scientific compute, simulation, and large language models (LLM)
- ECC memory: error correction for long-run stability
- Drivers: specialized professional software instead of consumer video drivers
- RTX 6000 Ada Generation: 48 GB GDDR6, 384-bit bus, Ada Lovelace architecture
- RTX PRO 6000 Blackwell: 96 GB GDDR7, 512-bit bus, PCIe 5.0 support
- Infrastructure: server cabinets and racks, switches, and a closed network contour
Software for LLMs and knowledge bases
An open-source stack for local training, inference, and RAG inside the company perimeter.
- Models: Llama, Mistral, Qwen, DeepSeek and their open-source variants
- Inference: vLLM, TensorRT-LLM, Ollama, llama.cpp
- Training and fine-tune: PyTorch, Hugging Face Transformers, PEFT / LoRA / QLoRA
- RAG and agents: LangChain / LlamaIndex, embeddings, vector DBs (Qdrant, Chroma, Milvus)
- Orchestration: Docker, Kubernetes, FastAPI; GPU-load monitoring
AI agents: knowledge and automation
Corporate LLMs and knowledge bases inside the perimeter — without cloud APIs.
- Support AI agents on the company’s internal documentation
- Staff knowledge bases with search and source citations
- Automation of report and policy analytics
- Additional training and RAG only on your data, with no leak outside
Contour protection
- Full isolation from the public internet (air-gapped contour on request)
- Data residency: data and models stay inside the perimeter
- A closed contour with no cloud providers
Go deeper into focused solutions
How the work is structured
- 01
Audit
We visit, analyze processes, measure the server room, and lock data requirements.
- 02
Design and procurement
We write the spec, bring RTX cards, assemble racks from scratch, and install them in your cabinet.
- 03
Training and deploy
We write the software, fine-tune models on your data, run on-site stress tests, and hand over to operations.
Order an initial AI audit of your infrastructure
Our architect will review your business processes, calculate the GPU capacity you need, and draft a preliminary project estimate.
