A private company AI is a small, well-defined project, not a switch you flip. Choose one use case, pick a model and hardware to match, lock down access, test it on real work, and only then widen it. This overview covers the realistic path using Ollama, and where a managed service may be wiser.
Step 1: Pick one use case for your private company AI
Good first projects are narrow: summarising internal documents, answering staff questions from a policy handbook, or drafting replies to common enquiries. Avoid “AI for everything”. Write down what success looks like, and what data the system will touch.
Step 2: Choose the model and hardware
Ollama can run many open models, and the documentation describes a model library for coding, vision, embeddings and reasoning tasks. Larger models generally need more memory and processing power, so match the model to your hardware, and test a smaller one first. Check each model’s page for its requirements rather than guessing.
Step 3: Install and test on a single machine
Ollama supports macOS, Windows, Linux and Docker, and offers a local API plus Python and JavaScript libraries, per its GitHub page. Start on one machine, run your real prompts, and compare outputs against what a competent staff member would produce.
Step 4: Control access
- Don’t expose the service to the open internet without proper protection.
- Limit who can use it, with accounts and logging where possible.
- Keep the operating system and software updated.
- Back up configuration and any document stores.
Step 5: Connect it to your work
Once it behaves well, you can connect it to your workflows and tools, for example through n8n or a website assistant. Keep sensitive documents in a controlled store, and decide what the AI may read.
Step 6: Write the rules
Document what staff may and may not ask, who reviews outputs, and how to report problems. See keeping business data private when using AI for a starting policy.
When should you get help?
- You handle regulated or highly sensitive data.
- Many users will rely on it.
- You lack someone to maintain servers and security.
In those cases, a managed private deployment may be safer than a DIY setup. Compare approaches in Ollama vs ChatGPT, or start with what a private LLM is.
A private company AI works best when it starts small. We help plan and implement private AI through our related services. Contact us with your use case.
Frequently Asked Questions
How long does a private AI pilot take?
A small pilot on one machine can be quick, but proper testing, security and rollout take longer. Plan in stages.
Do I need a GPU?
It depends on the model and speed you need. Smaller models can run on modest hardware, while larger ones benefit from more capable machines.
Can it answer questions from our own documents?
Yes, with additional setup that gives the model access to your documents. Control what it can read and test for wrong answers.
Is it secure by default?
No. Security depends on your configuration, network exposure, access control and updates.
What does it cost to run?
Costs include hardware, power, setup and maintenance. Estimate them against the value of the use case before you start.