Ollama makes running open-source LLMs locally as simple as a single command, enabling organizations to deploy AI assistants without sending data to third-party APIs. It supports models from Llama 3, Mistral, Gemma, and Phi families with automatic model management, GPU...
ZTABS builds on-premise ai assistants with Ollama — delivering production-grade solutions backed by 500+ projects and 10+ years of experience. Ollama makes running open-source LLMs locally as simple as a single command, enabling organizations to deploy AI assistants without sending data to third-party APIs. It supports models from Llama 3, Mistral, Gemma, and Phi families with automatic model management, GPU acceleration, and an OpenAI-compatible API that makes migration from cloud LLMs seamless. Get a free consultation →
500+
Projects Delivered
4.9/5
Client Rating
10+
Years Experience
Ollama is a proven choice for on-premise ai assistants. Our team has delivered hundreds of on-premise ai assistants projects with Ollama, and the results speak for themselves.
Ollama makes running open-source LLMs locally as simple as a single command, enabling organizations to deploy AI assistants without sending data to third-party APIs. It supports models from Llama 3, Mistral, Gemma, and Phi families with automatic model management, GPU acceleration, and an OpenAI-compatible API that makes migration from cloud LLMs seamless. Ollama's Modelfile system lets teams customize models with system prompts, parameters, and adapter layers without retraining. For enterprises with data residency requirements, HIPAA compliance, or air-gapped networks, Ollama provides the fastest path to production AI assistants.
All inference runs on your hardware. No data leaves your network, no prompts are logged by third parties, and no API keys are needed. This satisfies data residency regulations, HIPAA requirements, and defense sector mandates.
Ollama's API matches the OpenAI chat completions format. Existing applications using the OpenAI SDK can point to Ollama with a base URL change—zero code modifications required for basic chat and completion flows.
Modelfiles define system prompts, temperature settings, context windows, and stop sequences per use case. Create specialized assistants for HR, legal, engineering, and support from the same base model.
Ollama automatically detects NVIDIA, AMD, and Apple Silicon GPUs, applying optimal quantization and batch settings. Models run at full GPU speed with automatic memory management and model swapping.
Building on-premise ai assistants with Ollama?
Our team has delivered hundreds of Ollama projects. Talk to a senior engineer today.
Schedule a CallUse Ollama's keep_alive parameter to control model unloading. Set it to "24h" for your primary model to keep it in GPU memory, avoiding the 10-30 second cold start on first request. For rarely used models, set keep_alive to "5m" so they free GPU memory quickly for more active models.
Ollama has become the go-to choice for on-premise ai assistants because it balances developer productivity with production performance. The ecosystem maturity means fewer custom solutions and faster time-to-market.
| Layer | Tool |
|---|---|
| LLM Runtime | Ollama |
| Model | Llama 3.1 70B / Mistral Large |
| RAG | LangChain + ChromaDB |
| Backend | FastAPI |
| Frontend | Next.js + Vercel AI SDK |
| Auth | LDAP / Active Directory |
An on-premise AI assistant deployment uses Ollama running on GPU servers within the corporate network, serving a Llama 3.1 70B model quantized to 4-bit for optimal performance-to-quality ratio. FastAPI wraps Ollama's API with authentication via corporate LDAP, rate limiting per user, and audit logging of all interactions. RAG pipelines use LangChain to embed internal documents into ChromaDB, retrieving relevant context for each query before sending to the LLM.
Department-specific Modelfiles configure separate assistant personalities—the legal assistant uses conservative language with citation requirements, while the engineering assistant allows technical jargon and code formatting. The Next.js frontend uses the Vercel AI SDK's useChat hook pointed at the internal FastAPI endpoint, providing a familiar chat interface with conversation history stored in PostgreSQL. Model updates are managed through Ollama's pull mechanism from an internal model registry, allowing controlled rollouts.
Multiple models can be loaded simultaneously with automatic memory management, serving different departments from a single GPU server.
Our senior Ollama engineers have delivered 500+ projects. Get a free consultation with a technical architect.