Local & On-Premises AI
Private LLMs, Self-Hosted Models, and AI That Keeps Your Data In-House
AI That Runs on Your Hardware, Not Someone Else's
You do not have to send your data to a third party to use AI. We deploy private, self-hosted models that run on hardware you control, so your documents, customer data, and prompts never leave your environment. For the right workloads, on-premises AI is more private, more predictable, and cheaper than paying per token to a cloud API.
Why Run AI On-Premises
Cloud AI APIs are convenient, but they come with three quiet costs. Your data leaves your control. Your bill scales with every token, so heavy use gets expensive fast. And the model, its pricing, and its terms can change underneath you at any time. Local AI removes all three: the model runs on your hardware, the cost is the hardware (not a meter), and nothing changes unless you change it.
This matters most for confidential work: legal documents, medical records, financial data, proprietary research, anything you would never paste into a public chatbot. With a local deployment, the answer to "where did our data go?" is simple: it stayed here.
Where Local AI Beats the Cloud
- Privacy and compliance - data stays inside your network, which simplifies legal, HIPAA-adjacent, and confidentiality requirements
- Cost at volume - batch and high-frequency workloads that would run up a large API bill cost almost nothing once the hardware is in place
- Latency and offline use - no round trip to a remote server, and the system keeps working without an internet connection
- Stability - the model does not get deprecated, throttled, or repriced without your say
What We Deploy
We build the whole stack, not just install a model:
- Open-weight language and vision models, chosen and tuned for your specific tasks
- Private RAG: a search layer over your own documents so the model answers from your knowledge, with citations
- An inference server with a clean API your existing applications can call
- Integration into the tools your team already uses, rather than one more place to log in
Right-Sizing the Hardware
The biggest waste in AI projects is buying more hardware than the job needs. A focused document or vision task can run on a capable workstation or a Mac with unified memory; a busy multi-user deployment may want a dedicated GPU server. We size the hardware to the workload and tell you honestly where the line is. As proof of how far small models go, we replaced a 765-line multi-engine OCR pipeline with a single 0.9-billion-parameter vision model running on a Mac, and the result was simpler and more accurate, with every byte staying on-premises. The full write-up is here: from broken OCR to a searchable archive.
Match the Model to the Task
You rarely need the biggest, most expensive model for every step of a job. We routinely split work so a smaller, cheaper model handles the bulk and a larger one is reserved for the hard parts. On one client engagement, restructuring the workflow this way cut their AI bill by 80 percent. The same discipline applies to local deployments: pick the smallest model that does the job well, and the economics take care of themselves. Background reading: cutting AI costs with a two-model workflow.
Private RAG: Your Documents, Your Model
The single most useful local AI deployment for most businesses is a private assistant that knows your own material. We index your documents, contracts, manuals, archives, tickets, and connect them to a local model that answers questions grounded in that content and links back to the source. The whole system, index and model alike, lives on your hardware. It pairs naturally with a custom intranet so the people who need answers have one place to ask, and with your data architecture so the index stays current.
Use Cases
- Document search and Q&A - ask plain questions across years of internal files and get cited answers
- Extraction and classification - pull structured data out of invoices, forms, and scans at volume
- Summarization - condense long reports, transcripts, and threads
- Support drafting - generate first-draft replies grounded in your own knowledge base
- OCR and archives - turn scanned and image content into searchable text on-premises
Frequently Asked Questions
What is local AI?
Local AI means running AI models on hardware you control: a workstation, a server in your office, or your own cloud instance, instead of sending your data to a third-party API. The model runs in-house, so prompts, documents, and outputs never leave your environment. It is also called on-premises, self-hosted, or private AI.
Why run AI on-premises instead of using ChatGPT or a cloud API?
Three reasons: privacy, cost, and control. Your data stays in-house, which matters for legal, medical, financial, and any confidential work. There are no per-token bills, so heavy or batch workloads stop being expensive. And you are not exposed to a vendor changing the model, pricing, or terms underneath you. For the right workload, local AI is both cheaper and safer.
Do I need expensive GPUs to run a model locally?
Not always. Many useful models run well on a capable workstation or even a Mac with unified memory. We size the hardware to the job: a small vision or language model for document tasks needs far less than a large general-purpose model. We will tell you honestly what your use case actually requires rather than overselling hardware.
Can a local model answer questions about my own documents?
Yes. We build private retrieval-augmented generation (RAG) systems that index your documents and let a local model answer questions grounded in them, with citations back to the source. Your knowledge base stays entirely on your hardware. This is one of the highest-value local AI deployments for most businesses.
Will a local model be as good as the big cloud ones?
For broad, open-ended reasoning the largest cloud models still lead. But for focused business tasks (document search, extraction, classification, summarization, OCR, support drafting) a well-chosen open-weight model running locally is often more than good enough, and it wins on privacy and cost. We match the model to the task instead of defaulting to the biggest one.
Want AI Without Sending Your Data Away?
Tell us what you would use AI for and what data is involved. We will tell you whether a local deployment fits, what hardware it needs, and what it would cost to own instead of rent.