How we build with AI · Private / Local AI Engineering
PrivateAIengineering,inacontrolledenvironment
AI-powered software delivery in a controlled processing environment. We set up and evaluate privately hosted models, agree the access boundaries, and build your software with the same design, QA and release discipline as every NPCoding project, with optional ongoing management of the models and infrastructure.
We clarify the requirements, select and evaluate privately hosted models on your actual tasks, and set up the infrastructure with agreed access boundaries. Then we deliver the work: engineering, UI/UX, QA and controlled releases, reviewed by the same senior people as any other project.
It's a tailored engineering engagement, not hardware rental: the value is in choosing, configuring and operating the environment well. After launch, we can keep managing the models and infrastructure as an ongoing service.
Is this the right service?
Choose private / local AI when
We're building or changing your software, and AI processing must stay on infrastructure you control
Contracts, regulation or internal policy rule out third-party AI processing of your code or data
You want the environment set up for the project, and optionally managed afterwards
Consider instead
Private AI Infrastructure if you need the private model environment itself, for your own team, without a development project.
Clarify what must stay private, why, and which parts of the work it applies to.
02
Model selection & evaluation
Open-weight models compared on your tasks for capability, speed and cost, so the trade-offs are known before you commit.
03
Infrastructure setup
Hosting in your environment or cloud account, configured for the workload, with logging and monitoring.
04
Access boundaries
Network rules, permissions and audit logging, agreed and documented with you.
05
Expert delivery
Engineering, UI/UX and QA carried out in the private environment, with every change reviewed.
06
Release & ongoing management
Controlled releases with rollback, plus optional ongoing management of the models and infrastructure.
Need only the private model infrastructure?
If your own team will use the environment and no software needs building, Private AI Infrastructure covers the design, setup, evaluation and operation on its own.
A documented design of where the models run, who can access them and what connects to them.
02
Model evaluation
Results from testing candidate models on representative tasks, with a recommendation.
03
A working environment
The private AI environment, set up and tested, then handed over or managed by us.
04
Delivered software
Your software, built, reviewed, tested and released by NPCoding engineers, designers and QA.
(04)How the package works
Howwedeliverprivate/localAI.
01
Clarify requirements
Establish what must stay private, under which agreements, and for which parts of the project.
02
Evaluate models
Compare candidate models, hardware and hosting on representative tasks, and agree the trade-offs.
03
Set up the environment
Build the environment with documented access boundaries, logging and monitoring.
04
Build, review & test
Engineers, designers and QA deliver the work inside the environment, with every change reviewed.
05
Release & manage
Release under change control, then patch, monitor and update models as agreed.
(05)How we build
Expertengineers.ThebestAIharness.
Every project is built by our engineers in a proven AI engineering harness, then reviewed, tested and released by people. You choose where the AI runs: leading cloud tools or privately hosted models. The team, the standards and the accountability stay the same.
Package 01
Private / Local AI Engineering
AI-powered software delivery in a controlled processing environment.
For projects with specific processing requirements. We set up and evaluate privately hosted models, agree the access boundaries, then design, build, test and release your software inside that environment.
A senior engineer as your HR for AI, managing the agents
Privately hosted models, evaluated on your own tasks
Infrastructure setup with agreed access boundaries
Expert engineering, UI/UX and QA
Controlled releases with review and rollback
Optional ongoing management of the private models and infrastructure.
AI-accelerated delivery with Claude Code and OpenAI Codex, directed by experienced engineers.
Our engineers use Claude Code and OpenAI Codex for analysis, implementation, refactoring, testing and documentation, choosing the right tool for each task. We run the workflow and take responsibility for delivery, so you never manage coding agents yourself.
A senior engineer as your HR for AI, managing the agents
AI-assisted codebase analysis and planning
Implementation and refactoring, reviewed by engineers
Tests and documentation with every change
Architecture, UI/UX, QA and DevOps behind each release
Continued development, maintenance, QA and operations, as scoped.
ComparePrivate / Local AI EngineeringClaude Code & Codex Engineering
Suitable requirementsPrivate / Local AIProjects whose contracts, regulation or internal policy call for AI processing on controlled infrastructure.Claude Code & CodexNew builds, modernization and ongoing development where approved cloud AI tools fit your policies.
AI processing environmentPrivate / Local AIOpen-weight models hosted privately, on infrastructure you control or that we set up for the project.Claude Code & CodexClaude Code and OpenAI Codex, processed by each provider under the account and settings approved for your project.
Setup and infrastructurePrivate / Local AIModel selection and evaluation, hosting and access setup, scoped before development starts.Claude Code & CodexNo AI infrastructure to build: we bring an established, reviewed workflow into your repositories.
Data and access arrangementsPrivate / Local AIAccess boundaries, logging and network rules agreed and documented with you.Claude Code & CodexAccess limited to the repositories and data the work needs; provider terms and settings confirmed with you.
Expert oversightThe same in both: a senior engineer acts as HR for AI, directing and reviewing all AI-assisted work, and designers, QA and DevOps own their parts of every release.
Ongoing managementPrivate / Local AIMaintenance, QA and operations as scoped, plus optional management of the private models and infrastructure.Claude Code & CodexContinued development, maintenance, QA and operations, as scoped.
The package decides where AI processing happens while we build. Where your finished application runs is planned separately, in your cloud account or another environment agreed for the project.
When contracts, regulation or internal policy rule out processing your code or data with third-party AI services, even under business terms. For many projects, approved cloud tools with the right account and settings are enough. We'll help you work out which applies.
Is a private environment automatically more secure?
Not automatically. It changes who controls the processing and the infrastructure. Security still depends on access control, patching, monitoring and how the whole system is operated, which is why those are part of the package.
Does private mean total isolation, or that our data stays in Canada?
Only if the architecture is designed that way. We document where each component runs, who can access it and what it connects to. Isolation and data residency are established by that architecture and your agreements, not assumed, so we confirm them for each project.
Will a private model be as capable as Claude Code or Codex?
Often not on complex work, and it may be slower. Privately hosted models can work very well for well-defined tasks. We evaluate candidates on your actual work before you commit, so the trade-off is clear.
Is this a GPU rental service?
No. It's an engineering engagement: we scope, set up and evaluate the environment, build your software with it and, if you choose, manage it afterwards. The hardware can run in your own environment or cloud account.
Can you manage the models and infrastructure after launch?
Yes, as an optional ongoing service: monitoring, patching, controlled model updates and access reviews, with responsibilities agreed per engagement.
If an AI coding tool runs on our machines, does the AI run there too?
Not necessarily. Many AI coding tools run locally but send prompts and code to a cloud model for processing. Private processing requires the model itself to run on infrastructure you control, which is what this package sets up.
Does private hosting make us compliant?
No single technical choice does. Private hosting gives you more control over processing, which can support your privacy and contractual obligations. Compliance also depends on your policies, your agreements and how the whole system is operated, so we work alongside your legal and privacy advisors.
If AI stays on private infrastructure, must our application be self-hosted too?
No. Private / Local AI Engineering is about where AI processing happens while we build. The finished application can run in your cloud account, on your own servers or in another environment agreed for the project; production hosting is planned separately.
