The board wants AI. Legal said no to ChatGPT. Both are right.
You handle PHI, financial records, or privileged client data. Cloud LLMs are off the table — and the vendors who said otherwise haven't met your compliance officer. Yet the board still wants an AI answer. We deploy language models on your hardware, inside your firewall: air-gapped if needed, HIPAA / SOC 2 in scope from day one.
What compliance-bound teams are saying
Real phrasing from the people this page is for. If several sound familiar, you're in the right place.
"Sending even anonymized logs to a third-party API is a non-starter. You're handing your crown jewels to someone else's premises."— Engineer, after three GDPR audits
"If your compliance officer cannot explain where inference logs are stored, you are not sovereign."— Industry analysis, 2026
"All the prompts have been tuned for OpenAI."— Enterprise CIO, discovering the new lock-in
"What I spent in 2023 I now spend in a week."— CIO, on cloud token costs
"Prompt injection remains a frontier, unsolved security problem."— CISO of a major cloud-LLM provider
"Implementing private, secure AI isn't merely an option — it's a critical necessity."— Director of Digital Innovation, healthcare
What another quarter of "evaluating options" costs
Shadow AI spreads
While the official answer is "wait," employees paste PHI and privileged data into personal ChatGPT accounts nobody sanctioned or logs. The risk you're avoiding is already happening — unmanaged.
Token bills compound
Cloud LLM economics punish success: the better adoption goes, the worse the invoice. "There's no world in which pricing doesn't significantly evolve" — their words, your budget.
The mandate escalates
Boards don't forget AI line items. Every quarter without a working answer erodes credibility on the one topic every director now tracks.
From board mandate to production — without the science project
Free readiness review — async, NDA-first
Describe your data estate, compliance frame, and use cases. We reply with honest workload triage: what needs sovereign, what genuinely doesn't. Deliverable: architecture direction + compliance mapping against HIPAA / SOC 2 / ISO 42001. Including "cloud with the right controls is fine for your case" when that's true.
Pilot on your hardware — 6–8 weeks
One high-value workload in production conditions: typically RAG over your internal documents on open-weight, Llama-class models. Runs on your GPUs or a reference appliance we spec. Your team gets hands-on access from week two.
Production & partnership
SSO (Okta / Azure AD), audit logging, monitoring, model updates, quarterly capability reviews — month-to-month. You own the stack; we keep it sharp. Same partnership model as our legacy practice: longest client 3+ years.
Pilot: typically $50K–$80K. Hardware reality: a dual-GPU appliance for 70B-class models runs $15–25K — the review includes exact TCO against your projected cloud spend.
Your realistic options, honestly compared
| What your compliance review will ask | Private LLM (us) | ChatGPT / Claude Enterprise | Big-4 consultancy build |
|---|---|---|---|
| Where do prompts and inference logs physically live? | Your hardware, your building | Vendor cloud — their retention, their court orders | Configurable |
| Can it run fully air-gapped? | ✓ Native mode | ✗ | Possible |
| Token costs at scale | Flat — pennies per 1K tokens | "No world in which pricing doesn't evolve" | N/A |
| Vendor lock-in risk | Open weights — swap any layer | "All prompts tuned for one API" | Proprietary frameworks |
| Who integrates your legacy data estate? | ✓ Our core business | You do | Junior staff, billed senior |
| Engagement cost | Free review → $50–80K pilot | Per-seat + tokens, compounding | $500K–$2M+, 6–18 months |
Proof over promises
The hard part of private AI isn't the model — it's the plumbing
Your useful data lives in a 12-year-old ERP, three undocumented SQL databases, and a file server nobody mapped. A private LLM is only as good as its access to that estate. Deploying a model on a GPU takes a week; making it useful and compliant against your real data is systems-integration work — the muscle we've built for years.
Pattern worth knowing: Reference stack: open-weight Llama-class models · vLLM inference · pgvector RAG · LiteLLM gateway · SSO · full audit logging. Every layer swappable. You can fire us and keep it all running — that's the point.
Two questions before anything else
No discovery-call theater. Answer async; our architect replies within 4 business hours with a first read on your situation and an honest workload triage.
- ✅ What needs sovereign vs. what honestly doesn't
- ✅ Hardware + engagement ballpark before commitment
- ✅ Compliance-framework mapping for your industry
Start the free review
The questions your CISO will ask
Aren't open-weight models worse than GPT-class models?
For generic chat — sometimes. For your workloads — usually not: a Llama-class model fine-tuned on your domain, with RAG over your documents, routinely beats a frontier model that's never seen your data. "Good enough + sovereign + flat-cost" beats "slightly better + compliance risk + escalating tokens" for regulated work.
What does the hardware actually cost?
Less than the fear suggests: a dual-GPU appliance capable of 70B-class models runs $15–25K. A serious 8×H100-class cluster is $250K+ — and very few mid-market workloads need that on day one. The free review includes exact sizing with TCO math; on-prem typically wins above ~50% utilization.
Can this really be HIPAA / SOC 2 compliant?
The architecture makes compliance simpler: data never leaves your controlled environment, so entire categories of vendor-risk questions disappear. We deliver the technical controls — access, encryption, audit logging, isolation — mapped to your framework, and document everything auditors ask about.
We have no ML engineers. Who maintains this?
The partnership tier: monitoring, model updates, prompt-pipeline maintenance, quarterly reviews — month-to-month. Because the stack is open and documented, you can hire internally later and take over everything. No hostage situations.