Your Data Never Leaves Your Network.
Most AI tools ship your customer records, contracts and case files to somebody else's servers. We deploy open-weight models on your own hardware or private cloud, then fine-tune them on your data. You get the capability without handing anything over, and you keep the model when we're done.
Nothing crosses the line.
Prompts, documents and outputs stay inside the perimeter you approve. Air-gapped if that's what your policy requires.
Book A Private AI Assessment →You're not slow to adopt AI. You're just not allowed to leak data.
Legal teams, clinics, financial firms, defence suppliers, HR departments, anyone operating under NDA: the interest in AI is already there. What stops the project is a data processing agreement nobody will sign, or a client contract that says your files stay on your systems.
A private deployment removes that conversation entirely. The model runs where your data already lives, so there is no third party to vet, no retention policy to argue about, and no vendor that can change the terms underneath you.
From locked down to live in weeks.
No pilot that runs for a year. We scope it against your security policy, prove the quality on your own tasks, and then put it into production.
The result, and the guarantee.
The result
The work your team repeats every day, drafting, extracting, summarising, classifying, handled by a model that speaks your vocabulary. Sensitive material stays in-house, your cost stops scaling with every request, and the system keeps running if a vendor changes pricing or policy.
The guarantee
We benchmark before you spend anything on hardware. If a private model can't reach the quality bar on your use case, we tell you plainly and point you at the cheaper answer, even when that answer isn't us. We would rather lose a project than sell you a downgrade.
Four pieces. One private stack.
Most companies need two or three of these, not all four. We scope it to the workflow that's actually costing you time.
Private LLM Deployment
An open-weight model installed and served inside your environment, with the API your applications talk to sitting behind your own firewall.
- Sized against your real concurrency, not a guess
- Standard OpenAI-compatible endpoint for easy integration
- Runs air-gapped when policy requires it
- Access control and audit logging you own
Fine-Tuning On Your Domain
Prompting steers a general model. Fine-tuning teaches it your formats, your terminology and your edge cases, so output is consistent instead of nearly right.
- Training set built from your existing documents
- Shorter prompts, lower cost per request
- Consistent structure your systems can parse
- Retrained on a schedule as your business shifts
Private Knowledge Retrieval
Your manuals, contracts, tickets and policies made answerable, with a vector index that lives on your infrastructure alongside the model.
- Answers cite the source document
- Respects your existing permission model
- No documents uploaded to an outside index
- Refuses to answer rather than inventing
Evaluation & Guardrails
A private model without evaluation is a liability. We build the scoring harness that tells you when quality drifts, before your users find out.
- Reference test set built with your experts
- Automatic scoring on every model change
- Redaction and topic boundaries enforced in code
- Human approval steps wherever risk is high
Three ways to keep it private.
Private does not have to mean a server room. It means your data is processed inside a boundary you control, and we'll tell you which of these fits your policy and budget.
On-premise hardware
A GPU server in your own rack or office. Nothing touches the public internet, which is the only option that satisfies a genuinely air-gapped policy.
Your private cloud
The model runs in your own AWS, Azure or GCP account, inside your VPC and under your existing security controls. No capital spend, no new hardware to look after.
Hybrid routing
Sensitive requests go to the local model, everything harmless keeps using a hosted API. The cheapest way to get most of the benefit without moving your whole stack.
Open weights. No lock-in.
We pick the model per task, from the open-weight families that can legally and practically be self-hosted.
The weights sit on your storage. If you stop working with us tomorrow, the system keeps running and you can hand it to any competent engineer. That is the difference between owning a capability and renting one.
Worth doing for some companies. Overkill for others.
This is for you if…
- You handle data under NDA, HIPAA, GDPR or client confidentiality
- Legal or security has already blocked a public AI tool
- You have repetitive document, support or analysis work worth automating
- Your API spend is climbing and won't stop
- You want a system that survives a vendor changing its terms
This isn't for you if…
- You're comfortable sending your data to a hosted API
- Your volume is low enough that per-token pricing is simply cheaper
- You need frontier-level reasoning on wide open-ended problems today
- You want it live next week with no evaluation step
- Nobody internally will own the system after handover
Engineers, not resellers.
We're not reselling somebody else's platform with a markup. We build and operate the system, then hand you the keys.
12+ years running 7th Pillar Infotech, building platforms, products and custom AI tooling across industries. Leads model selection, fine-tuning and deployment architecture.
7thpillar.com ↗
A decade running service businesses in the US. Works out which parts of your operation are actually worth automating, so the build targets real hours instead of a demo.
emmanuelonate.com ↗Before you book
Direct answers. No corporate fluff.
Is an open model actually as good as GPT or Claude?
It depends entirely on the task. On narrow, well-defined work such as extraction, classification, summarising your documents or drafting in your house format, a fine-tuned open model is often just as good and far cheaper to run. On wide open-ended reasoning, the hosted frontier models are still ahead. That is exactly why we benchmark on your tasks before you commit to anything.
What hardware do we need?
It depends on model size and how many people use it at once. Plenty of workloads run comfortably on a single workstation-class GPU. We size it against your real usage during the assessment and give you the number before you buy anything.
Can it run completely offline?
Yes. Once deployed, the model runs locally with no outbound calls at all. Updates and retraining happen on your schedule, applied by you or by us on your network.
Do you train models on our data?
Your data trains your model and nothing else. It never enters a public model, and it never leaves the environment you approve.
How is fine-tuning different from just writing better prompts?
Prompting steers a general model at runtime. Fine-tuning changes the model itself so it already knows your formats, terminology and edge cases. The practical result is more consistent output, much shorter prompts, and a lower cost per request.
We already use a hosted API. Do we have to rip it out?
No, and usually you shouldn't. A hybrid setup is common: sensitive workloads route to the private model, everything else carries on using the API. You get the compliance win without a rebuild.
Who owns the model when we're done?
You do. The weights, the fine-tuned checkpoints, the training data and the deployment configuration are all yours, sitting on your storage. If you stop working with us, nothing switches off.
How does pricing work?
A fixed-scope build so you know what you're buying, plus your own infrastructure cost. Ongoing care and retraining are optional and separate. There is no per-token bill from us.
Find out if private AI is worth it for you.
Bring us the workflow and the constraint. We'll tell you which model class can handle it, roughly what hardware it takes, and whether a private deployment beats simply using an API. If it doesn't, you'll hear that too.
Or reach us at hello@rethinkai.now