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KELIX Enterprise AI Platform

Proprietary private cloud AI engine built for 6,000+ engineers on multi-billion dollar projects.

Proprietary private cloud AI engine trained on engineering standards, project history, and compliance frameworks. Deployed where public cloud AI was ruled out by security and compliance requirements. Multimodal capabilities covering text, vision, and structured data, with full audit trails built in from day one rather than bolted on after deployment.

The Challenge

Over 6,000 engineers were buried in legacy documentation and siloed knowledge across multi-billion dollar infrastructure projects. Generic AI models lacked the domain specificity for complex infrastructure standards. Public cloud AI tools were ruled out by security and compliance requirements.

The Approach

Built a secure private cloud AI platform using RAG architecture, trained on engineering standards, project history, and regulatory compliance frameworks. Integrated multimodal capabilities for text, vision, and structured data. Implemented enterprise-grade security, full audit trails, and governance controls from day one of deployment.

The Outcome

KELIX powers knowledge access and workflow automation across 6,000+ engineers. Significant reduction in document retrieval time and cross-referencing workload. Full compliance maintained across multi-billion dollar project environments, and the platform's architecture was carried forward directly into the Meinhardt AI Centre of Excellence programme.

SME Takeaway

Domain-specific AI trained on your own work outperforms generic models on your specific tasks every time. If your business handles confidential client data, proprietary processes, or regulated information, a private cloud deployment is not a luxury; it is the only defensible choice. The architecture scales down to any team size.

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