KyperX AI

KyperX AI  /  Solutions  /  Private AI

Private AI

AI infrastructure designed around your information, systems and security requirements.

For organisations that cannot simply send their information to a default AI service. KyperX designs and implements private AI environments and private LLM deployments, from document retrieval to models served inside your own boundary, and selects the architecture from your requirements and jurisdiction rather than from a preferred vendor.

Plan a Private AI Deployment →Architecture options ↓
Pricing
Scoped to requirements
First stage
Discovery and architecture decision
Architectures
Controlled APIs, private cloud, dedicated, on-premises or hybrid
Decided by
Your data, access, location and operating requirements

Based in Australia? Private AI for Australian organisations, in AUD →

When it is needed

Why default AI tools are sometimes not enough.

Usually one or two requirements rule out the standard option. Establishing which ones genuinely apply is what determines the design.

01

Confidential or regulated information

Client, patient, financial, legal or personal information that your policies or regulators restrict.

02

Where data may be processed

Rules about the countries or regions in which information may be processed or stored, including logs, backups and support access.

03

Intellectual property

Designs, source code, research or methods that must stay inside an environment you control.

04

Segregation and audit

AI that respects existing permissions, keeps business units or clients separate, and records who asked what.

05

Internal systems

AI that must reach document stores, databases and applications that are not exposed to the internet.

06

Predictable capacity

Sustained workloads where dedicated capacity may be easier to plan and govern. Whether it costs less depends on the workload.

Architecture options

From controlled APIs to hardware you own.

Private AI does not have to mean on-premises. The recommended option is the simplest one that meets the requirements that genuinely apply.

Private AI architecture options and trade-offs
OptionWhat it isTrade-offs
Commercial APIs under defined controlsCommercial models used under enterprise terms, with data-handling settings and regional processing where the provider offers them.Fast to adopt, with strong models. Depends on the provider’s terms and on which regions offer the required models; the provider stays in the data path.
Private cloudModels, retrieval and supporting services deployed in your own cloud account or tenancy, in a region you select.Strong control without buying hardware. Costs scale with use, and not every model or service is available in every region.
Dedicated capacityHardware reserved for your organisation, hosted in a data centre.Isolation without ownership. Lead times and a capacity commitment; locations depend on the hosting provider.
On-premisesHardware you own, inside your own network and facilities.Maximum physical control. Capital cost, power, cooling and operational responsibility sit with you or an agreed operator; hardware procurement and import lead times vary by country.
HybridSensitive workloads kept private; less sensitive workloads sent to controlled APIs.Balances capability and control. More design work, and the routing rules need governance of their own.

Deployment and ownership compared position by position: Infrastructure and owned deployment →

Reference layering

How a private AI environment fits together.

A way of reasoning about the environment. Which components exist, and where each runs, is decided per engagement.

  1. People and applications

    Search and question interfaces, assistants inside existing tools, and APIs for other systems.

  2. Identity and access

    Your identity provider, role-based access, and source permissions carried through to every answer.

  3. Orchestration and retrieval

    Retrieval over approved sources, policy and prompt controls, and narrowly scoped tool use.

  4. Models

    Open-weight models served privately, commercial models under defined controls, or both, chosen by evaluation.

  5. Information sources

    Document stores, databases and business applications, connected through governed connectors.

  6. Compute and hosting

    Cloud region, dedicated capacity, on-premises hardware or a combination.

Reference layering, not a product. Read top to bottom, from the user to the hosting.

Design decisions

Knowledge, models, compute and integration.

Made from evidence gathered during discovery and architecture.

01

Organisational knowledge

Retrieval and document intelligence over your own material, with cited sources and permissions respected. Answer quality depends heavily on document preparation, so it is tested on your documents.

02

Model choice

Driven by the task, required accuracy, languages, document types, context length and licence terms, and compared on representative tasks before a decision.

03

Compute sizing

Derived from users, concurrency, document volumes, response-time targets and availability needs. Compute and infrastructure explains how requirements scale.

04

System integration

Connectors to internal applications designed so failures are visible and existing permissions are not widened.

Controls

Controls, designed and documented for your review.

Your security, privacy and risk teams review the design against your own obligations. KyperX does not certify security or compliance.

Control areas in a private AI environment
Control areaWhat is designed and documented
Identity and accessAuthentication through your identity provider, roles, and how source permissions are enforced in results.
SegregationHow users, teams or clients are kept apart in data, indexes and logs.
Audit and loggingWhat is logged, where logs are stored, who can read them and how long they are kept.
Data locationWhere data, embeddings, logs and backups reside, and whether any processing leaves the chosen region.
Network boundariesInbound and outbound paths, including any external calls and the terms they operate under.
Remote access for deliveryHow KyperX engineers reach the environment during build and support, and what your team performs directly.
Evaluation and operationsQuality monitoring, model updates, patching, incident response and change approval, whether run by your team or under a separate arrangement.

Engagement

Discovery, architecture, implementation, handover.

Each stage is scoped and quoted before it starts, and nothing is procured until the architecture is agreed.

  1. 01

    Discovery

    Requirements, users, information flows, jurisdictional constraints and systems.

    Output: requirements record

  2. 02

    Architecture

    Options compared, trade-offs stated, recommended design with indicative costs.

    Output: architecture decision

  3. 03

    Implementation

    Environment, connectors, controls and applications, built as agreed.

    Output: working environment

  4. 04

    Validation

    Evaluation on representative tasks; documentation for your security review.

    Output: evaluation results

  5. 05

    Handover

    Rollout support, documentation and training. Ongoing operation can be arranged separately.

    Output: operable system

Do your requirements rule out a default AI service?

Describe what the environment should do and the constraints that apply. We will outline the options worth considering.

Plan a Private AI Deployment →

Questions

Questions about private AI.

What security, risk and technology leads ask first.

Can our data stay in our country or region?

Often, but it has to be confirmed rather than assumed. It depends on which cloud regions, model providers and supporting services are available where you operate, and on where logs, backups and support access sit. Discovery maps each of these before an architecture is recommended.

Does KyperX need access to our data from Australia?

Not necessarily. Access during build and support is designed with your security team. Where information may not be accessed from outside your jurisdiction, the work can be structured so your staff perform the steps that touch it.

Can you help with GDPR, HIPAA or similar requirements?

KyperX designs and documents technical controls against the requirements your organisation and its advisers identify. It does not provide legal opinions or compliance certification.

Are open-weight models good enough?

For some tasks, yes; for others a commercial model is clearly better. Candidates are compared on tasks drawn from your own work before one is chosen.

Can KyperX operate the environment afterwards?

Ongoing operation can be arranged as a separate agreement, subject to the access model your security requirements allow. Handover is designed so your team can run it instead.

Enquiry

Plan a private AI deployment.

Describe what the environment needs to do, the information involved, and the security, access or data-location requirements that apply.

What happens next

  1. We review your enquiry. We read what you have described and check whether it is work KyperX can usefully do.
  2. We reply by email. Usually with a few questions, or a suggested time for a short conversation where there appears to be a fit.
  3. Scope is confirmed in writing. If an engagement is appropriate, you receive a written scope and fee before any work begins. There is no commitment until then.

Prefer email? Write to ai@kyperx.com.

Enquiry · Private AI · Scoped to requirements

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