AI Infrastructure for Real-World Deployment

High-performance inference capability designed for agent-driven workloads, delivered as a scalable platform.

Modern AI is no longer a single interaction. It is a continuous, multi-step process driven by intelligent agents operating across data, systems, and workflows.

This shift requires a new class of infrastructure — built for speed, scale, and economic efficiency.

ADD delivers this capability as a deployable AI platform.

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An infographic depicting AI inference infrastructure, featuring a server hardware component with labels highlighting high throughput, economics at scale, large context windows, deployable hardware, and an illustration of a digital galaxy background.
Two animated figures, a woman with brown hair in a ponytail and a man with dark skin and a shaved head, sit at a white round table with a laptop open in front of them. Both wear corporate attire with logos on their shirts.

What we provide

ADD delivers a complete AI infrastructure capability designed for performance, scalability, and real-world deployment.

  • High-speed inference for real-time and agent-driven AI applications.

  • Scalable infrastructure supporting advanced and large-scale models.

  • Deployment within existing data centre environments, including air-cooled and power-constrained sites.

  • Integration with enterprise systems and workflows, enabling rapid adoption without disruption.

  • Full-stack orchestration via unified API access, providing control, flexibility, and operational efficiency.

  • Sovereign AI capability, with deployment aligned to geographic, regulatory, and organisational requirements.

  • Energy-aware infrastructure, with integration of power management and renewable energy strategies where sustainability is a priority.

Delivery Model

Structured deployment aligned to sovereign, enterprise, and energy-integrated environments

120-Day Deployment Timeline

ADD delivers AI infrastructure on a defined deployment pathway, enabling organisations to move from planning to operational capability within 120 days.

Phase 1: Design & Planning (Days 0–30)

  • Requirements definition and use case alignment

  • Infrastructure design and site assessment

  • Integration planning and deployment architecture

Phase 2: Build & Integration (Days 30–90)

  • Hardware deployment and configuration

  • Platform integration with enterprise systems

  • API setup and orchestration layer implementation

Phase 3: Deployment & Activation (Days 90–120)

  • System validation and performance optimisation

  • Operational readiness and testing

  • Go-live and transition to production

A digital infographic depicting a 120-day AI deployment timeline divided into three phases against a purple, starry background. Phase 1 features two cartoon characters in construction helmets examining tools, representing design and planning. Phase 2 shows a character in a construction vest waving, symbolizing build and integration. Phase 3 displays a group of professionals, including a woman with a headscarf and a man in a hard hat, representing deployment and activation.