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Zero Data Risk · GDPR-Compliant · No API Costs

Private AI — Your Company's Brain. On Your Server.

A language model trained on your documents, running entirely on your infrastructure. Zero data ever leaves your building.

ChatGPT knows nothing about your products, contracts, or clients. And every time your team uses it — that data goes to OpenAI's servers. Private AI fixes both problems: deep knowledge of your business, full data residency.

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The Problem

Your team knows the answer is somewhere in the 4,000 documents you've accumulated over the years. Finding it takes an hour. Asking ChatGPT risks a data breach. So the question either gets answered slowly, or not at all.

What We Build

We deploy a private language model — Mistral, Llama 3, or Phi — on your own VPS, connected to your documents via a RAG pipeline. Your team asks questions in plain language. The model answers from your actual data. No per-query costs. No third-party data processors. Works offline.

What You Get
✓Local LLM deployment (Ollama + model of your choice)
✓RAG pipeline on your documents (PDF, Word, Excel, Notion exports)
✓Vector database setup (pgvector or Qdrant)
✓Chat interface: Telegram bot or web UI
✓Document ingestion pipeline — add new docs without re-deploying
✓Full source code and documentation
✓30-day post-launch support
How It Works
01
Document audit

We review what you have — SOPs, contracts, product docs, support history. Define the knowledge base scope and expected query types.

02
Build and deploy

We set up the VPS, deploy the LLM, build the RAG pipeline, and wire it to your chat interface. Typical build time: 5–10 days.

03
Test and hand off

You run real queries. We tune retrieval and chunking until answers are accurate. Full docs and walkthrough delivered.

Real Use Cases
Internal HR & policy Q&A bot — employees ask, it answers from the employee handbook
Sales assistant trained on your product catalog and pricing
Legal contract review — clause extraction without sending docs to external servers
Customer support bot — 70–80% of tickets resolved without human review
Prozorro tender analysis in 30 seconds from a 200-page procurement doc
Pricing
$2,500–$6,000
one-time

Scoped during the free assessment. You get a fixed quote before any commitment.

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Frequently Asked Questions
What hardware does Private AI require?+
Minimum: 16 GB RAM, 4 vCPU (Hetzner CCX23, ~$50/month). For larger knowledge bases or concurrent users: 32 GB RAM (CCX33, ~$100/month). These are your direct infrastructure costs — separate from our deployment fee.
Can it answer in multiple languages?+
Yes. Multilingual models (Mistral, Llama 3) handle Ukrainian, Russian, English, and other languages. The model answers in the same language as the question.
What document formats do you support?+
PDF, Word (.docx), Excel (.xlsx), Google Docs exports, Confluence and Notion exports, plain text. Typical knowledge bases range from 50 to 10,000+ documents.
How is this different from the AI Knowledge Base tier?+
The AI Knowledge Base tier uses cloud LLMs (Claude, GPT-4) with your data as context — fast, lower cost, but your queries pass through external servers. Private AI runs the model entirely on your server — zero external API calls, required when data residency is a hard legal requirement.
Does it need internet access to work?+
No. Once deployed, the system runs fully offline. It only needs internet during initial setup for model download.
See It In Action
→Case study: Building an AI consultant with corporate memory (RAG + n8n)→n8n vs Zapier: data sovereignty and self-hosted AI
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