Service
Private AI deployment in Vancouver — your data never leaves your hands
NextAgent, based in Richmond, BC, deploys private AI on servers in Vancouver or Greater Vancouver. Customer data stays on infrastructure you control, not on a public cloud, and helps you meet Canadian privacy law requirements.
What is private AI deployment?
Private AI deployment means running AI on your own servers or in a Canadian data center, instead of using shared public cloud services like ChatGPT or Claude's API. Customer files, medical records, and legal documents never leave infrastructure you control. For Vancouver law firms, clinics, and financial businesses handling sensitive data, many choose it to keep that data under their own control.
NextAgent, a Richmond, BC agency, installs open-source AI models (Llama 3, Mixtral, and others) on your hardware with full encryption, access controls, and a complete audit trail. You own everything: the data, the model configuration, and all outputs.
If you are searching for private AI deployment in Vancouver or Richmond, BC, the practical choices are on-site servers or co-location in a local data center. We help with either approach, and we scope the work in a written proposal so you know what hardware and support are included before you commit.
Private vs. Cloud AI: The Real Difference
Cloud AI services are convenient but create data risks. Private deployment keeps data on infrastructure you choose and supports your compliance work.
Enterprise edition: OpenClaw (ClawPro)
On your intranet
ClawPro is the privately deployed edition of OpenClaw. The application runs on servers inside your corporate network, so data stays on premises. It suits finance, tax, manufacturing and law firms.
What it covers
Multi-employee accounts and permissions, integration with enterprise systems (ERP, CRM, OA, finance) and single sign-on, custom business-automation skills, security auditing, a private knowledge base and white-label branding.
Compliance
The architecture is designed to support MLPS (等保) Level 2–3 requirements for industries such as finance and law; a formal assessment is a separate process.
Related case studies
Real deliveries, systems still running, clients anonymised.
Private AI Deployment Questions
Is private AI deployment difficult to maintain? Do I need an in-house ML team?
No. NextAgent handles deployment, updates, monitoring, and optimization. Our team in Richmond, BC provides ongoing support and proactive management. You don't need internal ML expertise. We provide dashboards and APIs so your existing development team can integrate private AI into applications without becoming MLOps experts.
What hardware do I need for private AI deployment in Vancouver?
It depends on model size and query volume. Small models (7B parameters) run on modest GPU servers; larger models (70B+ parameters) require enterprise GPU clusters. We assess your needs and recommend cost-optimal hardware. Many Vancouver businesses co-locate servers in local data centers (like Equinix Vancouver) rather than buying on-site, and we help with either approach. We publish no price list for this service yet: it is priced by team size after a paid pilot, and hardware is bought at cost with no markup.
What is private AI deployment, and how is it different from using ChatGPT?
Private AI deployment runs an AI model on servers you control, either on-site or in a Canadian data center. With ChatGPT or a cloud API, your prompts and documents are sent to the vendor's servers. With a private deployment, they stay on your infrastructure, protected by your own encryption, access controls and audit logs.
Can a Vancouver law firm, clinic or immigration office use private AI with client files?
Yes, this is one of the most common reasons businesses choose private AI. Because documents are processed on infrastructure you control, sensitive client and patient files are not sent to a public AI service. Compliance still depends on your overall setup and policies, so we review your workflow with you before deployment.
Does private AI help with PIPEDA compliance in Canada?
It helps. Keeping personal information on infrastructure you control, with encryption, access controls and an audit trail, supports PIPEDA obligations. It does not make a business compliant on its own, because compliance depends on your overall practices, consent processes and security setup.
How much does private AI deployment cost?
Cost is driven by the model size you need, how many people and queries it must support, the hardware (on-site or co-located) and ongoing support. We do not publish a price list for this service. Pricing is set by team size after a paid pilot, and hardware is bought at cost with no markup. Book a free consultation and we will scope it in a written proposal.
Further Reading: Related Services
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