Service

AI running on your own server —your data never leaves your hands

NextAgent deploys AI systems on servers in Vancouver or Greater Vancouver. Customer data stays on your infrastructure, not on a public cloud, and meets 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 — not using shared public cloud services like ChatGPT or Claude's API. Customer files, medical records, and legal documents never leave your infrastructure. For Vancouver law firms, clinics, and financial businesses handling sensitive data, this isn't optional — it's the only compliant approach. NextAgent installs the latest 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.

Private vs. Cloud AI: The Real Difference

Cloud AI services are convenient but create data risks. Private deployment is secure and compliant.

Data Sovereignty
Cloud AI (ChatGPT, Claude API, Google Cloud AI):Data leaves your infrastructure
NextAgent Private AI Deployment:Data never leaves Vancouver/BC
Compliance Risk
Cloud AI (ChatGPT, Claude API, Google Cloud AI):PIPEDA, HIPAA concerns
NextAgent Private AI Deployment:Fully PIPEDA & HIPAA compliant
Long-term Cost
Cloud AI (ChatGPT, Claude API, Google Cloud AI):$0.01-0.10 per API call (compounding)
NextAgent Private AI Deployment:Fixed capex + modest opex
Model Customization
Cloud AI (ChatGPT, Claude API, Google Cloud AI):Limited to vendor's base model
NextAgent Private AI Deployment:Full fine-tuning and custom training

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 Vancouver-based team provides 24/7 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. Most clients dedicate 0.5 FTE for support coordination—that's it.

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 ($10-20K capex). 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—we help with either approach. Budget typically ranges $15K-$80K for hardware, $2-5K monthly for infrastructure + support.

Tell us about your situation

In 30 minutes, tell us what slows you down. We will say what we can do, roughly how long it takes, and whether a grant can help.