Private AI infrastructure, managed for you.
biggertech.ai designs, deploys, and runs AI your business owns — on infrastructure we set up and operate for you, end to end. Your core data stays under your control, your costs stay predictable, and you never hand your operations to a third-party API. No in-house IT team required.
Private AI used to demand an enterprise budget. It no longer does — so whether you run a lean team or a large organization, you can own the AI behind your operations instead of renting it. We handle the hardware, the deployment, and the day-to-day running; you keep the control.
The problem usually isn't the model. It's that your operations were never built to use it.
Teams rarely stall on AI because the models are weak. They stall because knowledge is scattered, permissions are unclear, exceptions break the flow, and nothing connects into one accountable system. We close those gaps with AI you own and control — not one more tool on the pile.
We deliver business-grade AI consulting, not a stack of generic feature cards.
We identify the exact parts of your operation where AI should assist, decide, coordinate, or execute — then turn those into measurable workflows tied to business outcomes.
Our method is grounded in operational reality — not AI theater.
The systems we design must be fast, accountable, and maintainable. We typically work through four phases, each with explicit outputs, governance boundaries, and operating KPIs.
Owning your AI only matters once it's doing the work.
The hard part was never the model. It's connecting AI to your systems, grounding it in your knowledge, and keeping it accountable to real outcomes. Our forward-deployed engineering closes that last mile — then hands you a system your team owns and runs.
We don't just argue for owning your AI. We run ours that way.
Systems we designed, built, and keep running in production — not prototypes, not slideware. Client work is described without naming the client, and everything here is live today.
Representative use cases where owned, operational AI creates leverage.
The strongest use cases sit in knowledge-heavy operations and cross-system execution — where keeping data and decisions under your own control matters most.
Trust isn't a feature we bolt on. It starts when the AI runs inside your boundary, with governance built around it.
AI introduces failure modes traditional security was never designed for. Our answer starts with architecture: your data, models, and decisions stay inside your perimeter — then permissions, logging, fallbacks, and audit are layered on top.
Questions, answered.
Direct answers to what businesses ask us most about owned, private AI.
What does “AI you own” actually mean?
The models, data, and workflows run on infrastructure you control — on-prem or your private cloud — with no dependency on a third-party API. You hold the keys, the data never leaves your boundary, and your cost is fixed compute, not a metered bill that grows with usage.
Is private AI only for large enterprises?
No. Private AI used to require an enterprise budget; compact local hardware has changed that. We right-size deployments so a lean, owner-operated business can run capable private AI — and grow it into a governed, multi-department system as it matures.
How is this different from using ChatGPT or a cloud AI API?
Public APIs send your data out, bill you per use, and can change price or policy at any time. We deploy AI that runs inside your environment: predictable cost, no data leaving your control, and workflows governed with permissions, logging, and audit.
What is AI Search Visibility (GEO), and do I need it?
AI answer engines like ChatGPT and Perplexity increasingly influence which businesses get discovered and shortlisted. GEO structures your site, metadata, and FAQ schema so those systems can read and cite you reliably — your content strategy stays yours; we make it machine-readable. It is the same method we apply to our own site.
How do you start an engagement?
We begin with a focused diagnosis of one workflow: map the process, the data, and the decision points, then define a small pilot with clear outputs and governance boundaries. From there we deploy and expand into higher-value operations.
Do we need an IT team to run this, and how is it priced?
No in-house IT team required. We deliver private AI as a turnkey, managed setup — we install, monitor, and update it remotely, so you own the system without the operational overhead. Hosting runs on infrastructure you choose, including Canadian data residency when required, and pricing is a predictable setup-plus-support model rather than a per-token bill that grows with usage. Engagements begin with a free 20-minute fit call; the AI Readiness Diagnostic is a fixed C$3,800, and implementation is quoted at a fixed price once the diagnostic has defined the scope.
How long does a first deployment take?
A focused first pilot typically goes from diagnosis to a working, governed deployment in a few weeks — not months. We scope it deliberately small so you see real results early, then expand from there.
Where does our data live, and can you meet compliance needs?
Your data lives on infrastructure you choose — on-prem or your private cloud — so it can stay within your jurisdiction, including Canadian data residency. Because the AI runs inside your boundary, meeting requirements around data location, access control, and auditability is part of the architecture, not an add-on.
About biggertech.ai
biggertech.ai is for organizations — lean teams and large ones alike — that want AI embedded in real operations and kept under their own control. We act as an operating partner for owned, governed AI: from diagnosis and design to private deployment and day-to-day running.
Implementation is scoped after the diagnostic and quoted at a fixed price before any work begins. Private deployments are engineered to your workload rather than sold off a price list, and hardware is a real part of the investment — the diagnostic is what turns that into a specific number.