Your Organisation's AI. On Your Infrastructure. Under Your Control.
Private AI systems that run entirely inside your organisation — your data never leaves your network, no dependency on third-party AI services, and full control over how AI is deployed, used, and governed.
Cloud AI vs. Private AI
Data Boundaries
Cloud AI: Every prompt, document, or conversation uploaded to a cloud AI service leaves your organisation's network. Your data is processed on someone else's infrastructure, under someone else's data policy.
Private AI: Every query stays on your hardware. No data crosses your network boundary. Nothing leaves your control. Your confidential information — customer records, financial data, business strategy — never touches a third-party server.
Platform Control
Cloud AI: You depend on a provider's API availability, pricing, and model roadmap. Models get deprecated. Pricing changes. Terms of service update. Your AI operations are tied to decisions made by someone else.
Private AI: You own the infrastructure, choose the models, and control when and how they're updated. No API dependency. No surprise pricing changes. No roadmap uncertainty. Your AI capability stands on its own.
Regulatory Compliance
Cloud AI: Processing personal information through external AI services creates legal exposure that most organisations haven't fully assessed — especially when employees use consumer-grade tools with company data. Cross-border data transfers, third-party processing, and audit trails become complex compliance questions.
Private AI: Your AI operates entirely within your compliance boundary. Data never crosses borders. Processing happens on your own infrastructure under your own governance. Compliance with POPIA, ODPC, NCSA+RURA, and sector regulations is inherent in the architecture — not something to retrofit.
What Is a Private AI System?
Your organisation runs its own private AI stack — open-weight LLMs deployed on your hardware, inside your network, under your full control. We call it organisational AI sovereignty: you own the data, the model, the infrastructure, and the governance.
Local LLMs on Your Infrastructure
We deploy open weight, opensource models that are capable and specific for the use case on your own hardware — a server in your office, a co-located rack, or a private cloud VM. These open-weight models match or exceed proprietary API quality for most business tasks, and you decide when and how they're updated.
Your Data Never Leaves Your Network
Every query, every document, every conversation processed by the AI happens on your infrastructure. No data travels across the internet to a third party. No external service ever sees your or your clients' information. No API key to manage, no data usage policy to worry about.
Full Governance & Observability
You decide who can use the AI, what data it has access to, which models are deployed, and how outputs are monitored. Usage logs stay on your network. Prompt history stays on your network. Everything is auditable, and everything stays inside your organisational boundary.
Feasibility
Is Private AI Feasible for Your Organisation?
Private AI isn't just for enterprises with dedicated data centres. Modern open-weight models run on surprisingly modest hardware — and the investment often pays for itself within months compared to cloud API subscriptions.
Edge devices (Jetson / industrial PC) + central GPU server
~R50k–400k
✓ Feasible
In every case above, the total investment is a fraction of what the same organisation spends annually on cloud AI API subscriptions — and you own the infrastructure.
Disclaimer: All pricing figures are estimates based on hardware costs as of May 2026. Actual costs vary by region, vendor, deployment complexity, and configuration. These figures are provided solely to assess feasibility — not as a quotation or binding estimate. Contact us for a tailored assessment of your organisation's specific requirements.
What We Deliver
We help your organisation achieve AI sovereignty — from assessment through deployment to ongoing operations. Not sure what investment looks like? View our investment guide →
1. AI Exposure Assessment
We audit how your organisation currently uses AI — every department, every chatbot, every cloud API — and quantify your data exposure. We assess your compliance posture against POPIA (South Africa), the Data Protection Act 2019 / ODPC (Kenya), Law No. 058/2021 / NCSA + RURA (Rwanda), and sector regulations — then recommend the right private AI architecture for your specific needs.
Not everyone has AI-ready hardware sitting in a server room — and that's exactly why this service exists. We design and deploy the right infrastructure for your private AI system, whether you're starting from scratch or upgrading existing traditional compute infrastructure.
For organisations without existing AI infrastructure: We specify, source, and deploy everything you need — GPU servers, storage, networking, and backup power. A complete, turnkey AI-ready environment delivered to your premises, configured for your workloads, and ready to run from day one. See typical investment ranges →
For organisations with traditional IT infrastructure: We assess your current server and compute capacity and recommend targeted upgrades to support AI workloads — GPU acceleration, additional memory and storage, or network improvements. We work with what you have and add only what's needed for AI, avoiding unnecessary replacement costs.
What's included: Hardware specification and vendor sourcing, server room or rack planning, network segmentation for AI workloads, cooling and power assessment, on-site installation, cabling, configuration, and burn-in testing. You get a production-ready private AI infrastructure with a clear maintenance and upgrade path — without needing an in-house infrastructure team.
Build applications that run entirely on your own infrastructure: private RAG systems that search your internal documents, confidential AI assistants for your teams, compliance agents trained on your regulatory framework, internal productivity tools, and domain-specific fine-tuned models — all without data ever leaving your network. Explore AI-enhanced software →
4. Managed AI Operations
24/7 monitoring of your AI infrastructure, model updates and version management, guardrail maintenance, usage reporting across departments, SLA-backed support with on-call for critical issues, and regular compliance reviews. We handle the operations — you keep full ownership of the system. Get managed support →
Real infrastructure — in-house deployment by the Inovosystems team.
Regulatory Compliance
When your AI runs inside your network, compliance is a design feature — not an afterthought. Our private AI systems are architected to help you meet data protection requirements across African jurisdictions.
South Africa — Protection of Personal Information Act (POPIA)
South Africa's POPIA is enforced by the Information Regulator and establishes comprehensive requirements for the lawful processing of personal information.
Security Safeguards (s. 19) — Responsible parties must implement appropriate, reasonable technical and organisational measures to protect the integrity and confidentiality of personal information. Private AI deployments can improve visibility, control, and auditability of security safeguards.
Cross-Border Transfers (s. 72) — POPIA restricts transfers of personal information outside South Africa unless specified safeguards, protections, or legal bases apply. Processing personal information entirely within South Africa may reduce or eliminate the need for international transfers where no personal information leaves the jurisdiction.
Private AI Infrastructure Consideration — On-premises AI systems can support POPIA compliance by reducing reliance on external processors and improving governance and accountability. However, organizations must still comply with all POPIA requirements relating to lawful processing, data subject rights, retention, security, and accountability.
Kenya — Data Protection Act, 2019
Kenya's Data Protection Act, 2019 is enforced by the Office of the Data Protection Commissioner and incorporates many principles similar to those found in GDPR-style privacy frameworks.
Security Measures (s. 32) — Data controllers and processors must implement appropriate technical and organisational measures to protect personal data. Private AI deployments can provide greater visibility and control over security controls and data processing activities.
Cross-Border Transfers (s. 48) — Personal data transferred outside Kenya must be protected through appropriate safeguards and transfer mechanisms established under the Act and related regulations. Processing data locally may reduce or eliminate the need for international transfers where personal data remains within Kenya.
Registration Requirements (s. 30) — Certain categories of data controllers and processors are required to register with the ODPC. Maintaining clear documentation of AI infrastructure, processing activities, and data flows can support compliance and registration obligations.
Private AI Infrastructure Consideration — Private AI infrastructure can improve governance, auditability, and control over personal data processing, but organizations must still satisfy all obligations under Kenya's Data Protection Act and associated regulations.
Rwanda — Law No. 058/2021 on the Protection of Personal Data and Privacy
Rwanda's data protection framework is established by Law No. 058/2021 on the Protection of Personal Data and Privacy and is primarily overseen by the National Cyber Security Authority.
Security Measures (Art. 19) — Controllers and processors must implement appropriate technical and organisational measures to protect personal data. On-premise AI infrastructure can provide enhanced visibility, control, and auditability of security controls.
Cross-Border Transfers (Arts. 25–28) — International transfers of personal data are subject to restrictions and generally require adequate protection or another lawful transfer basis. Processing AI workloads locally may reduce cross-border transfer risks and simplify compliance obligations.
Data Protection Officer (Art. 23) — The law requires designation of a Data Protection Officer in specified circumstances. Private AI deployments that maintain detailed audit trails and processing records can assist DPOs in carrying out oversight and accountability functions.
Private AI Infrastructure Consideration — Private AI infrastructure may improve governance, transparency, and control over personal data processing activities, but organizations must still satisfy all requirements of Law No. 058/2021.
Nigeria — Data Protection Act 2023
Nigeria's Data Protection Act 2023 is enforced by the Nigeria Data Protection Commission, replacing the earlier NDPR framework with a comprehensive statutory data protection regime.
Cross-Border Transfers (Part XI, ss. 47–52) — Personal data may be transferred outside Nigeria where the destination jurisdiction provides an adequate level of protection or where approved safeguards are implemented, including contractual protections, binding corporate rules, or applicable statutory derogations such as explicit consent. Maintaining AI workloads and personal data within Nigeria may reduce cross-border transfer obligations but does not automatically eliminate them.
Registration and Compliance Obligations — Data Controllers and Data Processors of Major Importance (DCPMI) are subject to registration, reporting, and compliance obligations established by the Commission, including audit and accountability requirements.
Automated Decision-Making (Part X, s. 40) — The Act provides protections relating to automated decision-making and profiling. Organizations deploying AI systems should implement appropriate governance, transparency, and human oversight measures where required.
Private AI Infrastructure Consideration — Private AI deployments can improve visibility, auditability, security control, and governance of personal data processing activities, but organizations must still satisfy all applicable obligations under the NDPA.
Egypt — Personal Data Protection Law No. 151 of 2020
Egypt's Personal Data Protection Law (PDPL) No. 151 of 2020 establishes the Personal Data Protection Center under the Ministry of Communications and Information Technology as the primary supervisory authority for data protection matters.
Registration, Licensing and Representation Requirements — Organizations engaged in personal data processing may be subject to licensing, permit, registration, and local representation obligations under the law and its implementing regulations. These requirements should be assessed on a case-by-case basis.
Cross-Border Data Transfers (Arts. 14–15) — Personal data generally may not be transferred, stored, or shared outside Egypt unless the destination provides an adequate level of protection and authorization is obtained from the Center, unless one of the statutory exceptions applies (such as explicit consent, contract performance, medical necessity, legal claims, judicial cooperation, or public-interest grounds).
Data Security (Arts. 8, 13) — Controllers, processors, and Data Protection Officers must implement appropriate technical, administrative, and organizational security measures and comply with security procedures designed to protect personal data and prevent breaches.
Private AI Infrastructure Consideration — Hosting AI infrastructure within Egypt may reduce cross-border transfer challenges and provide greater control over security and compliance processes, but it does not by itself guarantee compliance with Egypt's PDPL, licensing requirements, or regulatory obligations.
Ethiopia — Personal Data Protection Proclamation No. 1321/2024
Ethiopia's Personal Data Protection Proclamation No. 1321/2024 is the country's first comprehensive data protection law and establishes a supervisory Authority responsible for oversight and enforcement. It incorporates many principles comparable to international data protection frameworks.
Registration and DPO Requirements — The Proclamation contains registration obligations for data controllers/processors and requires the designation of a Data Protection Officer as part of organisational compliance measures.
Cross-Border Data Transfers (Arts. 18–21) — Personal data may be transferred abroad where adequate protection exists or where specific legal grounds apply, including explicit consent, contractual necessity, public-interest considerations, legal claims, or protection of vital interests.
Security and Breach Notification (Arts. 42–43) — Data controllers and processors must implement appropriate technical and organisational measures, maintain compliance controls, and notify the Authority of personal data breaches within 72 hours of becoming aware of them.
Private AI Infrastructure Consideration — Private AI deployments may simplify governance, auditability, data residency management, security control implementation, and incident-response oversight, but they do not by themselves guarantee compliance; organisations must still meet all statutory requirements under the Proclamation.
Sector Regulations
Beyond general data protection law, your organisation may be subject to sector-specific regulations that impose additional requirements on how data is processed, stored, and secured. A private AI system operates entirely within your existing compliance boundary.
Financial Services
Financial institutions operate under extensive governance, auditability, security, and risk-management obligations. Private AI infrastructure can improve visibility, control, and auditability while reducing reliance on third-party processing providers.
Healthcare
Healthcare organizations must protect patient confidentiality and sensitive personal information. Private AI deployments can reduce disclosures to external service providers and provide greater control over how patient data is processed.
Government & Parastatal
Public-sector organizations often require strong security, sovereignty, auditability, and governance controls. Private AI deployments can support these objectives through controlled, transparent, and locally governed processing environments.
Mining & Energy
Mining and energy operators manage highly sensitive operational and industrial data. Edge and private AI deployments can help keep critical operational information close to the source while supporting security, reliability, and governance requirements.
Your internal data governance policies and industry-specific requirements also apply — and private AI fits naturally within all of them, because nothing leaves your control.
Ready to bring AI under your control?
We'll assess your organisation's current AI exposure, design your private system, and deploy it on your infrastructure.
