Local AI · On-Premise
Local AI and a sovereign neural network in a closed contour
LLM deploy on your GPU server: a corporate ChatGPT, an AI knowledge base, and a local AI agent inside the perimeter — for CIS data-residency, security, and sovereignty requirements.
from $5,682 · pilot 4–8 weeks · What the quote is made of · Pilot program · All pricing
Who this is for
This is not “a chat for a department.” This is enterprise AI infrastructure for large business, banks, industry, and companies with strict requirements for data residency, trade secrets, and local compliance across the CIS.
EnterpriseLarge business and corporations
You need your own AI contour: no leaks to OpenAI/Google, no sanctions lockouts, and access control over internal knowledge.
ComplianceRegulated and strategic sites
Local personal-data and critical-infrastructure rules in KZ, UZ, BY and the wider CIS — we close them with on-premise architecture, not a foreign SaaS API.
RegulationBanks, healthcare, industry
Personal data, trade secrets, and industry rules. AI runs locally — security and auditors see a controlled contour.
Compliance we design for (CIS)
We design the contour for local data-protection and security rules in your country from day one — so the solution passes security review and audit instead of “breaking” at approval.
Why foreign cloud AI will not do
For enterprise this is not a “convenience” question — it is law in your jurisdiction, business continuity, and control over data.
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Confidential-data leak
Sending data to a foreign cloud breaks local personal-data and trade-secret rules. Prompts can go into training of external models. Secrets become available to third parties.
On-premise AI contours
Dedicated URLs: perimeter and security, GPU/infrastructure, models + RAG + SLA. No mixing in “and also websites” on this branch.
GPU and infrastructure
GPU selection for the model and load, power, cooling, orchestration. Honest about supply lead times.
Hardware / GPUModels, RAG, and SLA
Local LLMs, a corporate knowledge base, 4–8 week pilot acceptance criteria, and ongoing support.
RAG and pilot
What hardware we procure
Official supply of top GPUs is limited — we work through parallel import and alternatives. We spec the stack for the job: from light models to enterprise fine-tuning of 70B+.
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NVIDIA for enterprise
For serious fine-tuning and models from 70B — H100, A100, or B200 when channels exist. For 8B–32B, A10G or RTX 4090 in a server build with turbine cooling is often enough.
Infrastructure around the servers
Putting a GPU in a rack is only part of the work. A corporate AI contour lives on cooling, power, fast card interconnect, and orchestration.
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Power and cooling
A server with 8x H100 easily draws 10–12 kW. The server room or data center often needs an upgrade. We calculate this at audit, not after procurement.
Software, models, and fine-tune
We assemble a working contour: a base model, additional training on your data, fast inference, and RAG over internal documents — all inside the perimeter.
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Base LLMs
Llama 3 / 3.1 / 4, Mistral / Mixtral, Qwen and other open-weight models. Multilingual stacks for Russian, Kazakh, Uzbek and English when the knowledge base needs it.
What to prepare for in advance
Honest commercial barriers of an enterprise project: hardware lead times, people, and regulation. We bake them into the plan so launch does not slip.
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GPU lead times and logistics
Cards travel slowly; logistics are complex. Repair parts also need to be planned early.
How we roll it out for enterprise
- 01
Requirements and contour audit
Security, compliance, personal-data residency, integrations, and target scenarios. We lock architecture you can defend in front of the client’s security team.
- 02
Design and supply
GPU, network, access, monitoring. We assemble the contour for your data center / server room / dedicated segment.
- 03
AI core deploy
Models, RAG, security policies, logging. Data does not leave the perimeter.
- 04
Handover and support
Training for your IT team, operating procedures, SLA. If needed — further agent and scenario development.
What the corporation gets
Your contour
AI inside the perimeter — no foreign clouds
Compliance
Local personal-data and critical-IT rules across the CIS accounted for
Control
Access, logs, and resilience with no API-lockout risk
Quote: On-Premise AI
from $5,682 · pilot 4–8 weeksWe price the software contour (models + RAG + access) separately from GPU procurement. Hardware and data center — a separate line after a load audit.
Pilot program
What’s included
- Model + RAG on your documents
- Roles, logs, perimeter policies
- Acceptance criteria and pilot report
Success criteria in the contract
- Answer quality with a correct source
- Escalation rate to a human
- Latency and access policy compliance
What we measure in the pilot
- Share of answers with a correct citation
- Human escalations
- Pilot-group satisfaction
| Package | What’s included | Price | Timeline |
|---|---|---|---|
| Contour architecture | Security/load audit, perimeter design, model choice, pilot and hardware plan | from $1,705 | 1–2 weeks |
| Local AI pilot | LLM deploy, RAG over your documents, access roles, logs — no foreign cloud | from $5,682 | 4–8 weeks |
| Prod + SLA | Cluster, monitoring, fine-tune, support; GPU by specification | by SoW | from 2–4 months+ |
Contour architecture
Security/load audit, perimeter design, model choice, pilot and hardware plan
from $1,705
1–2 weeks
Local AI pilot
LLM deploy, RAG over your documents, access roles, logs — no foreign cloud
from $5,682
4–8 weeks
Prod + SLA
Cluster, monitoring, fine-tune, support; GPU by specification
by SoW
from 2–4 months+
What the quote is made of
- Audit and architecture. Security, personal data, scenarios, integrations, latency requirements.
- Software contour. Inference (vLLM and more), RAG, API, roles, action audit.
- Models and fine-tune. Open-weight stack, LoRA/QLoRA when needed.
- Hardware / GPU. Selection and supply — separate quote after load profiling.
- Integrations. CRM/ERP/1C, SSO, corporate search.
- SLA and handover. Playbooks, IT training, monitoring.
What drives the price up
- Model size and users. 7–8B vs 70B+, concurrent sessions.
- Knowledge-base volume. Documents, update frequency, multilingual content.
- Compliance. Local personal-data and critical-infrastructure rules in your country (KZ/UZ/BY and wider CIS).
- Data center and network. Power, cooling, InfiniBand/RoCE for a cluster.
What’s not included
- GPU and server cost (priced after audit; parallel import when needed)
- Closed cloud LLM licenses (we do not use them in the contour)
- Data-center power upgrades outside the agreed scope
- Full legal personal-data policy audit without your security owner
Contract scope
- Pilot 4–8 weeks for a typical case: acceptance criteria fixed in the contract.
- Pilot data does not go to public foreign APIs.
- Quote: software and hardware are separated — no end-of-project surprise.
Describe data, users and constraints — we return contour design, range and a pilot plan.
FAQ
Technology infrastructure
Questions on local AI
Models and inference on your servers (on-premise / closed contour). Data stays inside the perimeter: prompts and the corporate knowledge base do not go to a foreign cloud.
We design for data residency and security rules in your country (KZ, UZ, BY and the wider CIS): personal data stays in a controlled contour with no cross-border transfer to public LLMs. Compliance is baked into the architecture from day one — not a Russia-only checklist.
Yes: LLM additional training / fine-tune (LoRA, QLoRA) on your data inside the perimeter, plus RAG. We spec the GPU server for model size.
Yes: chat over internal documents, access roles, and agents in a closed contour — sovereign AI, not public SaaS.
Let’s discuss a contour for your corporation
Local AI and an on-premise neural network: GPU server, closed contour, corporate knowledge base without foreign clouds.
