Manufacturing & IndustrialAI & GenAIOn-Prem LLM

On-Premise Deployment of Open-Source LLM

Secure AI with zero external data exposure

Client

Leading Manufacturer

Region

Global

Key Outcomes

Zero external data exposureEliminated cloud LLM API costsLow-latency enterprise AI

Business Challenge

The manufacturer wanted to make Generative AI available to internal teams without sending prompts, documents, or model outputs to an external provider. Public LLM APIs did not fit the organization's data-residency and compliance posture, and usage-based API charges made long-term operating costs difficult to predict. The platform therefore had to run inside the client's environment, support enterprise authentication, isolate sensitive workloads, and remain maintainable as open-source models and GPU requirements evolved.

Solution Overview

DIATOZ designed a private LLM platform around open-source models hosted on dedicated on-premise GPU infrastructure. Containerized inference services expose controlled internal APIs rather than giving applications direct access to the model runtime. Authentication and role-based authorization restrict who can use each capability, while deployment packaging separates model configuration from application code so models can be evaluated or replaced without rebuilding every consuming application. The resulting platform gives internal teams a reusable AI foundation while keeping prompts, enterprise data, and generated responses within the client's security boundary.

Architecture & Engineering

Open-source language models deployed on dedicated NVIDIA GPU infrastructure inside the client network

Dockerized inference services with versioned model and runtime configuration for repeatable deployments

Secure internal APIs that separate business applications from model-serving infrastructure

Enterprise authentication and role-based authorization applied before requests reach the model

Fine-tuning and model-evaluation workflows that use approved enterprise datasets without external transfer

Monitoring of GPU utilization, request latency, failures, and model-service availability

Technology Stack

LLMsNVIDIA GPUsDockerPythonEnterprise Security

Business Impact

Prompts, source documents, and generated content remain inside the client's controlled environment

The organization owns the model runtime and can choose when to upgrade or replace a model

Dedicated inference capacity removes per-request dependency on external LLM APIs

Local serving reduces network round trips for approved internal use cases

A shared private-AI platform gives multiple teams a governed path from experimentation to production

Why DIATOZ

DIATOZ combined AI platform engineering, GPU deployment, application security, and model operations to turn an open-source model into a governed enterprise service rather than an isolated proof of concept.