Private AI
Private & Local AI Setup
Some businesses cannot, or would rather not, send client files, financial records, or internal documents to a third-party AI service. Others are tired of usage bills that grow every month. Local AI is the alternative: open models such as Llama, Qwen, Mistral, and DeepSeek running on hardware you control, through tools like Ollama, LM Studio, and llama.cpp.
We build the full setup around those models. Open WebUI gives your team a private, ChatGPT-style portal with user accounts. Retrieval over your internal files, SOPs, and policies lets staff ask questions and get answers that cite your own documents. Self-hosted n8n connects the local models to your CRM, inbox, and other APIs, so the same private AI can power automations. Everything runs in Docker for clean updates, sits behind a VPN, and is hardened like any other server holding sensitive data.
We are upfront about the trade-offs. You pay for hardware or a GPU server instead of usage fees, and a capable GPU is not cheap. Open models that fit on business hardware are good at drafting, summarizing, and answering from your documents, but they are generally not as strong as the largest frontier models from OpenAI, Anthropic, or Google on complex reasoning. For many teams, a mix works best: local AI for sensitive data, a cloud model for everything else. We help you decide.
In short
What is private or local AI?
Private or local AI is an AI system that runs on hardware or a private server a business controls, using open models such as Llama, Qwen, Mistral, or DeepSeek, so prompts and documents are not sent to an outside provider. TechPit Solutions sets up the models with Ollama, a ChatGPT-style portal with Open WebUI, chat over internal documents, and self-hosted n8n behind a VPN.
- Best for law firms, accountants, clinics, and consultancies that handle confidential client information or have rules on where data goes.
- Deployed remotely on your hardware or a private GPU server after a free consultation, hardware sizing, and model tests on your real tasks.
Who this is for
- Law firms, accountants, clinics, and consultancies handling confidential client information
- Businesses with data rules that limit what can be sent to external AI providers
- Teams with steady, heavy AI use where per-token bills keep growing
- Companies that want staff to search and ask questions across internal SOPs and documents
- Organizations that want to self-host n8n and connect it to private AI
Problems we fix
- Staff paste sensitive information into public AI tools because there is no approved private option
- Clients or contracts restrict where your data can be processed
- AI API costs are unpredictable and rise with every new use case
- Internal knowledge sits in shared drives and PDFs that nobody can search properly
- A local model was installed on someone's laptop but nobody else can use it and it is not secured
What's included
What our Private & Local AI service includes
Requirements and hardware sizing
We work out which tasks you want to run, how many users, and which models suit them, then size the GPU, memory, and storage needed, on premises or on a private cloud GPU server.
Local model deployment
We install and configure Ollama, LM Studio, or llama.cpp with models such as Llama, Qwen, Mistral, or DeepSeek, tested on your real tasks so you know what quality to expect.
Private ChatGPT-style portal
We deploy Open WebUI with user accounts, roles, and approved models, so your team gets a familiar chat interface that runs entirely on your infrastructure.
Chat over your documents (RAG)
We index your internal files, SOPs, and policies in a private vector database so staff can ask questions and get answers that reference the source documents.
Self-hosted n8n and integrations
We deploy n8n on your server and connect it to the local models, your CRM, inbox, and APIs, so private AI can classify, summarize, and draft inside your workflows.
Docker, VPN access, and hardening
Everything runs in Docker with documented updates and backups. Access is through a VPN such as WireGuard or Tailscale, with firewall rules, TLS, and least-privilege accounts.
Benefits
Why it's worth doing properly
Your data stays with you
Prompts and documents are processed on hardware you control rather than sent to an outside AI provider.
No per-token API bills
After the hardware and setup, running more prompts does not add usage fees, which makes costs easier to predict for heavy use.
Answers from your own knowledge
Staff can query internal SOPs and documents in plain English instead of hunting through folders.
Room to grow
The same private setup can power a team chat portal, document search, and automations, and new open models can be added as they improve.
Process
How the work runs
- STEP 01
Assess use cases and data
We review the tasks, data sensitivity, user numbers, and any client or contract rules, then decide whether local, cloud, or a mix makes sense.
- STEP 02
Design and size the stack
We choose models, hardware or GPU server, storage, network access, and integrations, and show you the expected quality on sample tasks before you commit.
- STEP 03
Deploy and secure
We install the models, portal, retrieval, and n8n in Docker, set up VPN access and hardening, load your documents, and train your team.
- STEP 04
Monitor and maintain
We monitor performance and resources, apply updates, test newer models when they are released, and refresh your document index as content changes.
Tools we commonly work with
- Ollama
- LM Studio
- llama.cpp
- Open WebUI
- n8n (self-hosted)
- Docker
- Qdrant
- WireGuard
- Tailscale
- NVIDIA GPUs
FAQ
Private & Local AI: common questions
Are local models as good as ChatGPT or Claude?
Not on every task. The largest frontier models are generally stronger at complex reasoning, long documents, and difficult writing. Open models that run on business hardware are often good enough for drafting, summarizing, classification, and answering questions from your own documents. We test candidate models on your real tasks before you buy hardware, so you see the quality first.
What hardware do we need?
It depends on the models, the number of users, and the speed you expect. Small models can run on a well-specified workstation, while larger models and several simultaneous users need one or more GPUs with enough video memory. We size the hardware to your use case and can also set it up on a rented private GPU server if you prefer not to buy equipment.
Is local AI really cheaper than using an API?
Sometimes. You swap usage-based fees for an upfront hardware cost plus power, hosting, and maintenance. For heavy, steady use, local AI can work out more predictable. For light or occasional use, a business API plan is often cheaper and simpler. We give you an honest comparison based on your expected usage before recommending either.
Does running AI locally make us compliant with data laws?
Keeping data on your own infrastructure can help, but it does not make you compliant on its own. We implement the technical safeguards, such as access control, encryption, logging, and network isolation. Your legal or compliance advisor confirms which rules apply to your data and whether the setup meets them.
Can staff access the private AI remotely?
Yes, through a secure VPN such as WireGuard or Tailscale rather than exposing the server to the internet. Staff sign in to the Open WebUI portal with their own accounts, and access can be limited by role. We can also connect it to your existing sign-in system where supported.
How much does a private AI setup cost?
Cost depends on the hardware or GPU server, the number of users, the models, the volume of documents to index, and the integrations you need. Hardware is a separate purchase or rental. Book a free consultation and we will outline the options and give you a clear quote.
Available in
Delivered remotely in these countries and across Europe. Prices in USD.
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Talk to us about Private & Local AI
A free 15-minute call to understand what's slipping through the cracks. You'll leave with the department that fixes it first and what it costs, whether or not you work with us.