RAG over your documents, agents, and automation — on a single Apple Silicon machine you own. No cloud, no per-token bills, and your data never leaves the building. Specced, built, and handed over running.
Get a private-AI build recommendation, or check whether your AI use is insurance-ready — instant results, no lead-gen form in disguise.
Tell us your use case, data sensitivity, and team size — get a recommended private-AI build back instantly: model size, components, and the box to run it on.
Get My Build ConfigurationIs your AI use insurance-ready? Get your readiness score, the gaps your cyber insurer will flag, and a starter AI policy you can use today.
Start the free checkTell me your use case and I'll spec the right private-AI build with you — the model, the components, and the box to run it on.
Talk to meA quick answer in one slider — or open it up and build the exact number. Either way, see how fast a private box pays for itself against monthly AI subscriptions.
A handful of the most-requested builds. Each one is scoped to a 90-day ROI target, runs on your own appliance, and is delivered working. Most businesses start by classifying what they already have — then add the build that saves the most time.
Before anything else: the box reads every document you own and proposes a sensitivity class for each. You approve. Most businesses have never classified their data and can't say where the sensitive material actually lives.
A private assistant that answers from your own policies, contracts, manuals, and SOPs — with the source cited, in seconds.
Drafts replies to customer emails and support tickets from your knowledge base — your team reviews and sends.
Records, transcribes, and summarizes your meetings into clean notes and action items — running entirely on-site.
Pulls structured data out of invoices, forms, and POs and drops it straight into your accounting or ERP system.
Self-hosted models, your data, your network — the whole stack on one machine that sits in your office. It starts on a single Apple Silicon box (the affordable, prove-it-in-your-office tier) and scales to more boxes or bigger iron when you outgrow it. Built from running this exact stack every day, not a theoretical integration.
Every layer of a production-grade local AI stack, configured, integrated, and managed for you — serving platform, model, interfaces, agent framework, MCP tooling, and memory.
Hover any piece for a quick note, or click it for the full explanation. Wider than your screen? Swipe or scroll sideways to see the rest.
A private model running on your own hardware, sized to what you actually have.
Search over your own documents and data — indexed and queried entirely on your infrastructure.
On-prem agents and automation, built the same way we build and run our own.
Keep the whole stack observable and maintained without depending on any cloud dashboard.
Scope: One use case, one model, one server — prove it works before committing further.
Output: Working demo on your own hardware, plus a scaling recommendation.
Ideal for: Teams who want to see it running before scoping a full build.
Scope: Complete stack — model, memory/RAG, agents, and orchestration — sized to your environment.
Output: Production-ready private AI infrastructure, handed off with documentation.
Ideal for: Organizations ready to commit to a fully on-prem AI setup.
Scope: Model updates, monitoring, and maintenance after deployment.
Output: A stack that stays current without becoming your team's full-time job.
Ideal for: Teams without in-house ML/infra capacity to maintain it solo.
Pricing is scoped after the discovery call — depends heavily on existing hardware and use case.
Book a Discovery CallAnswer 5 quick questions about your use case, hardware, and team size — get a recommended model size, components, and serving setup back immediately. No email required to see your results.
RAG retrieves the right text. MCP lets an agent act. An ontology layer gives it governed meaning — a semantic model of your business's concepts, relationships, and rules. The agent maps every request to your real definitions, enforces your constraints, records its assumptions, and asks a question instead of guessing when knowledge is missing.
A reasoning agent that grounds meaning in an ontology, acts through your enterprise tools, and delegates to specialist agents — so its output is explainable and governed, not just fluent.
The same term means different things across CRM, billing, and support. An ontology pins one definition, so the agent reasons over your concepts — not a guess pulled from raw text.
Every action traces back to an ontology concept and an explicit rule. When knowledge is missing, the Gap Detector raises a question and the Assumption Recorder logs what it assumed — an audit trail by design.
The knowledge graph and rule layer wire into your agents via MCP, on your own infrastructure — the same local-AI stack above, with a semantic control layer on top.
Our take on the ontology-driven agent pattern — an emerging approach in enterprise AI. Further reading: Nayan Paul, "Ontology-Driven Agents: The Missing Layer for Enterprise AI." M4Quick Studios is independent and unaffiliated.
The private AI stack that runs your business — Open WebUI, CrewAI, Llama, Ollama — sits at the center, with your data as its core. Ziti protection layers nest in from the left to control who can reach it; agent-governance layers nest in from the right to control what may be done. Add only the layers your requirements demand.
Real, recognizable components at the core; each layer wraps them — centered on the data you're protecting.
A private AI is capable, so it runs under an explicit charter: you're the sole authority, it can't rewrite its own rules, every risky action waits for your sign-off, and some things it will never do — no matter who asks.
Every action falls into a risk tier. Green and yellow run automatically; everything below the line waits for your approval, and the absolute floor is never automated at all.
The agent acts only on your say-so — and it can't change its own rules, permissions, or this charter. Authority only flows down from you, never up.
It never enters credentials, moves money, or deletes data without a separate, explicit confirmation — no matter who asks or how it's phrased.
Anything above the lowest tier is recorded with what authorized it — an audit trail you can read without a technician, that nothing in the deployment can alter.
Structured against CSA Zero Trust, ISO/IEC 42001, and NIST AI RMF — the frameworks a security-conscious buyer already recognizes.
Alignment with CSA Zero Trust, ISO/IEC 42001, and NIST AI RMF is a structured head start toward those frameworks — not a certification claim. Every deployment ships with a full governance charter you own.
Same approach, pointed at your data. Most organizations have never classified theirs and don't know where the sensitive material actually lives. Your box fixes that: it reads every document, proposes a sensitivity class, and you approve — so your whole estate gets labeled, and the rules follow from there.
A simple scheme a business owner can actually reason about — the more sensitive the data, the tighter the rule on where it can go.
The box scans your whole estate — contracts, policies, spreadsheets, PDFs — entirely on your own hardware. Nothing is sent out to be read.
It flags each one — this contract reads Confidential; this file has SSNs, so Restricted — as a proposal, never a silent change.
You confirm or adjust the proposed classes. Nothing is applied until you sign off — the same propose → approve loop as the rest of the system.
Once classified, who can open each document — and whether it's ever allowed to leave the box — follows its class, automatically.
Data class and the agent's risk tier combine: the more sensitive the data, the higher the bar on what any action may do with it. Classifying your documents is the most common — and most useful — first step.
Most great AI work runs in the cloud — and some of it can't. When a client in a regulated or privacy-sensitive space says "this data can't leave our building," that's where I come in. I build the private, on-premise piece — a self-contained AI appliance on hardware they own — and hand it back running. I'm not trying to own the engagement; I'm the specialist you bring in for the part that has to stay local.
You keep the client and the roadmap. I deliver the on-prem AI capability that would otherwise stall the project.
Finance, insurance, healthcare, legal — anywhere data residency, air-gaps, or "no cloud" rules apply.
Specced, built, tested, and delivered running — with the box, the models, and the docs. No lock-in to me.
Before I build anything, we agree on what a win looks like for your business — one concrete number: hours saved a week, faster customer response, fewer errors, quicker onboarding. Then it's tracked from day one, so it's a measured decision, not a leap of faith.
One clear ROI target, tied to your numbers, agreed before a dollar goes into building.
You see progress toward that target every week — not a slide deck at the end of the quarter.
By 90 days it's clearly paying off — or you knew long before, and we adjust or stop. No sunk-cost theater.
The best use of this isn't shaving a few dollars — it's leverage: handle more customers, onboard faster, and free your team for the work that actually grows the business. And it's built to grow right alongside you.
Begin on a single box that fits today. Add capabilities and capacity when you're ready — never a rip-and-replace.
Do more with the team you have. The win is capacity and speed — measured as growth, which is exactly what we track.
Your appliance isn't frozen the day it ships. It learns your business and gets sharper over time — the same self-improving approach I build into everything.
No theory — just practical tools and outcomes that work.
Pick a track and send a note — I'll follow up to schedule a discovery/scoping call.