Solutions / Private LLM (Private GPT) — Corporate Language Model in Your Infrastructure

Private LLM is an in-house deployed language model designed for secure processing of corporate information, automation of text-based workflows, and creation of intelligent services. The solution ensures full data isolation, stable performance, and flexible adaptation to business tasks.

Purpose and Value of the Solution

Private LLM is integrated into the organization's infrastructure and is used for document handling, support automation, improved internal data search, and integration of AI assistants into key business processes.

• Full data isolation: on-premise, private cloud, corporate VPS
• Compliance with security and privacy requirements
• Ability to fine-tune on internal documents
• Cost reduction through automation of routine operations
• High performance and scalability

Technical Architecture

1. LLM Model

Supports modern open-source models:

• LLaMA 3 / LLaMA 2
• Mistral / Mixtral
• Falcon / Phi
• Gemma, Qwen, and other models

2. Infrastructure Stack

• vLLM or OpenAI-compatible server
• Docker / Kubernetes for orchestration
• CUDA / ROCm when using GPU
• Model optimization (8-bit/4-bit quantization: AWQ, GPTQ)

3. API Access

The system provides full REST / OpenAI-compatible API for integration with corporate systems, chatbots, internal portals, and microservices.

Functional Capabilities

Text Generation and Processing

• Creation of documents, reports, and technical specifications
• Rewriting, editing, and simplifying texts
• Preparation of templates and business materials

Data and Document Analysis

• Extraction of facts and key information
• Classification and structuring of data
• Analysis of regulations, contracts, and technical documents

Intelligent Search (RAG)

Supports Retrieval-Augmented Generation for working with corporate documents.

• Indexing PDF, DOCX, HTML, Wiki
• Vector databases: FAISS, Qdrant
• Separation of data and model to enhance security

Business Process Automation

• HR: resume analysis, candidate responses, FAQ
• Customer support: AI chat, request automation
• Sales: generation of proposals, emails, CRM texts
• Document workflow: checking, summarizing, template generation

Model Fine-Tuning

The model can be adapted to industry terminology and company specifics:

• Supervised fine-tuning (SFT)
• Preparation of specialized datasets
• Corporate system prompts and behavior settings
• Adaptation to narrow subject areas

Security

The solution complies with GDPR, NDA, and corporate information security policies. Data is not transmitted to external services and is not used for training global models.

Deployment Options

• On-premise — deployment on client servers
• Private cloud — isolated client cloud infrastructure
• Hybrid scheme — combined deployment model

Project Scope

• Requirements analysis and infrastructure audit
• Selection of model and hardware configuration
• Deployment and setup of LLM server
• API integration
• RAG search setup (if needed)
• Model fine-tuning
• Testing and optimization
• Employee training and technical support