Private AI Infrastructure
Private AI infrastructure, set up and operated for you
When your developers or products need AI models that run on infrastructure you control, we design the environment, set up model serving and access, evaluate candidate models on your work and, if you choose, operate it: updates, monitoring, capacity and cost.
What you get
- Models on your terms. You decide where models run, who can use them and what they can reach.
- Capability you've measured. Model choices are based on results from your own tasks.
- Operated, not abandoned. Updates, monitoring and capacity planning continue after setup, if you choose.
(01) Overview
Part of our DevOps & Managed Operations capability.
Some organizations want AI models available to their own developers, analysts or products without sending data to third-party services. That takes more than a downloaded model: serving, access control, monitoring, updates and a realistic view of capability and cost.
This service covers the infrastructure on its own. We design the environment with you, set up model serving behind your access controls, evaluate candidate models on representative tasks and document how it all works. Your team uses it for its own work, and we can operate it afterwards under agreed responsibilities.
If you also want us to build software inside that environment, that's the Private / Local AI Engineering package. Both start from the same honest assessment of what private models can and can't do for your workload.
Is this the right service?
Choose private AI infrastructure when
- Your own developers, analysts or products need AI models that run on infrastructure you control
- You want the environment designed, set up, evaluated and documented properly
- You may want it operated afterwards, without a development project attached
Consider instead
- Private / Local AI Engineering if you also want NPCoding to build or change software inside that environment.
- DevOps & Managed Operations if you need pipelines and operations for an application rather than model hosting.
(02)Deliverables
What's included in private AI infrastructure.
- 01
Requirements & sizing
What the environment must support (users, workloads, data sensitivity) and the capacity that implies.
- 02
Environment design
Network boundaries, model serving, access control, logging and backups, documented before anything is built.
- 03
Model serving setup
Open-weight models served behind your identity provider, with per-team access and usage limits.
- 04
Evaluation on your tasks
Candidate models compared on representative work for quality, speed and cost, with a recommendation.
- 05
Monitoring & capacity
Usage, latency, errors and hardware utilization tracked, with alerts and capacity planning.
- 06
Operation & updates
Patching, model updates under change control, access reviews and cost reports, as agreed.
(03)Environment boundary
What runs inside, and who can reach it.
An illustrative design for a private model environment. The real one is drawn from your requirements and documented before anything is built.
Your controlled environment (your hardware, or your cloud account)
- 01
Access gateway
Sign-in through your identity provider, per-team permissions and usage limits
- 02
Model servers
Open-weight models chosen from evaluation results, updated under change control
- 03
Connected data and tools
Only the sources you approve, read-only unless agreed otherwise
- 04
Logging & monitoring
Usage, latency, errors and cost, with alerts to the agreed contacts
Isolation and data residency come from this design and where it's hosted. We document both rather than assume them.
(04)AI & responsibility
How AI assists infrastructure work.
Where AI helps
- Drafting infrastructure-as-code and serving configurations for review
- Generating evaluation sets from your representative tasks
- Summarizing usage, latency and cost trends
- Reading logs to narrow down serving errors
What our experts own
- The environment design and its network boundaries
- Choosing models from the evaluation results
- Approving every change to the environment
- Access decisions and regular access reviews
(05)Our process
How we deliver private AI infrastructure.
- 01
Clarify needs
Who will use the models, for what, with which data, and under which agreements.
- 02
Design the environment
Boundaries, serving, access, logging and recovery, documented and agreed with you.
- 03
Set up serving & access
Build the environment and connect it to your identity provider, with every change reviewed.
- 04
Evaluate models
Test candidate models on representative tasks and agree which to run.
- 05
Operate & review
Patch, monitor, update models and review access and cost, as agreed.
(06)Connected capabilities
Connected work, one accountable team.
How the other capabilities support private AI infrastructure on a project.
DevOps & Managed Operations
The environment is run with the same change control, monitoring and recovery discipline as your applications.
QA & Release Assurance
Evaluation sets are re-run before each model update, so a new version doesn't quietly get worse at your tasks.
How we build with AI
Choose how AI is used while we build.
Need software built as well? The Private / Local AI package runs our engineering inside an environment like this one; the Claude Code & Codex package uses approved cloud tools instead.
Private / Local AI Engineering
AI-powered software delivery in a controlled processing environment.
Claude Code & Codex Engineering
AI-accelerated delivery with Claude Code and OpenAI Codex, directed by experienced engineers.
(07) Tools & technologies
- Open-weight models
- Self-hosted inference servers
- GPU infrastructure
- Containers
- Identity provider integration
- Infrastructure as code
- Monitoring & logging
- Evaluation suites
(08)Industries
Where this matters most.
(09)FAQ
Private AI Infrastructure: your questions.
How is this different from the Private / Local AI package?
This service delivers the infrastructure itself, for your own teams or products. The package uses an environment like this to build your software with AI assistance. You can start with one and add the other later.
Do we need to buy GPUs?
Not necessarily. The environment can run on hardware you own, on GPU instances in your cloud account, or on infrastructure agreed for the project. We size the options with you before anything is bought.
Will a private model match the cloud tools our developers already use?
Often not on complex tasks, and it may be slower. We evaluate candidates on your own work, so the trade-off is clear before you decide.
Does a private environment mean our data stays in Canada?
Only if it's designed and hosted that way. We document where each component runs and who can access it; data residency comes from that architecture and your agreements.
Can you operate the environment for us?
Yes, as an option: patching, model updates under change control, monitoring, access reviews and cost reports, with responsibilities agreed per engagement.
Other services
Capabilities
How we build with AI
Next step
Let's talk about your private AI infrastructure project.
Tell us what you're building. We'll come back with practical next steps, a realistic plan and an honest estimate.