Private by default — runs entirely on your servers or private cloud, so nothing leaves your network.
Per-token and per-seat fees. Run unlimited local inference on hardware you already control.
Open models — Llama, Qwen, DeepSeek, Mistral and more — swappable anytime with Ollama.
What a private, local LLM can do for your business
Once an open model runs on your own servers, your team gets ChatGPT-style AI without sending a word to a third party. Six things it unlocks:
Private ChatGPT for your team
A private, ChatGPT-style assistant staff use all day — drafting, summarizing and Q&A — with nothing leaving your network.
Chat with your documents
Answer questions over your own files, wikis and PDFs with private RAG, so staff get sourced answers instead of guesses.
Coding & developer assistance
Local code assistants for your engineers — completion, explanation and review on your codebase, kept fully in-house.
Automate back-office work
Summarize tickets, extract fields from PDFs and classify emails — repetitive AI tasks that run on your own box.
Build AI into your apps
Ollama exposes an OpenAI-compatible local API, so your apps and workflows call your model instead of the cloud.
Choose and control models
Run Llama, Qwen, DeepSeek or Mistral, pick sizes that fit your GPU, and upgrade on your own schedule.
Ready to build private AI you control?
Book a free 30-minute strategy session. We’ll map the fastest, most private path to a self-hosted AI setup for your use case — no obligation.
Why cloud AI APIs fall short for private work
Sending prompts to a hosted API means your questions, documents and customer data leave your network and sit with a third party — a hard stop for regulated or confidential work. You also pay per token forever, costs scale with usage, and you are locked to a vendor’s model, rate limits and retention policy. For steady internal workloads, that is the wrong trade.
How we set up Ollama in your environment
We install and harden Ollama on your server, workstation fleet or private cloud, then select and size the right open models for your hardware — Llama, Qwen, DeepSeek or Mistral — using quantization so they run well on the GPUs you already have. We configure Modelfiles and system prompts for your use cases, wire up Ollama’s OpenAI-compatible API so your existing apps switch over with a URL change, and stand up a private chat UI (Open WebUI) for your team.
From there we add authentication, logging, monitoring and backups, document the whole Ollama setup, and hand it over so your own team can run it day to day — no per-token meter, and no prompts or data leaving your network.
Why teams choose Ollama for a private LLM
Ollama is the fastest way to run open large language models on hardware you control. It bundles the model, its configuration and a runtime into a single tool, so your team runs a model with one command instead of assembling an inference stack by hand. That simplicity is why Ollama has become the default choice for businesses that want a private, local LLM without a research-lab-sized ops effort.
With Ollama you pull any model from its library — Llama, Qwen, DeepSeek, Mistral, Phi and Gemma — then run it just as easily. Models ship as quantized GGUF files that fit the GPU (or even the CPU) you already own, and a simple Modelfile lets you set system prompts, parameters and behaviour per team or task. Because Ollama serves an OpenAI-compatible API, the apps your team already uses point at your Ollama endpoint with just a URL change — no rewrites — and Open WebUI gives everyone a private, ChatGPT-style interface.
What you get with Ollama, and where we help:
- One-command model management — pull, run, swap and upgrade Llama, Qwen, DeepSeek and more, with several models hosted side by side.
- Runs on your hardware — quantized models sized to your GPUs, from a single workstation to an on-prem cluster or private cloud.
- OpenAI-compatible API — a drop-in endpoint so existing apps switch to your local Ollama with a URL change.
- Private by design — prompts, documents and models never leave your network; we add authentication, logging and backups.
- Tuned per use case — Modelfiles and prompt templates configured for each team, plus Open WebUI for non-technical staff.
NeuralChainAI installs and hardens Ollama, selects and sizes the right models, integrates the Ollama API into your workflows, and hands you a documented Ollama deployment your own team can run.
What we build with Ollama
Private ChatGPT for your team
A secure, multi-user chat assistant on your own infrastructure for daily drafting, summarizing and Q&A. See our private & on-premise AI approach.
Chat with your documents (private RAG)
Connect the model to your files and wikis so staff get sourced answers from your own knowledge base. Explore private RAG — chat with your documents.
App & workflow integration
We connect Ollama’s OpenAI-compatible API to your apps, internal tools and automations, so features that used a cloud API now run locally. Part of our self-hosted AI stack.
Model selection, hardware & tuning
We help you pick models and quantization to fit your GPU and budget, then tune throughput and context length. New to local models? Start with our guides on running an LLM locally and local-LLM hardware requirements.
Frequently asked questions
Ready to run a private LLM on your own servers?
Tell us your use case and hardware, and we’ll map the fastest path to a private, local LLM your team can rely on.