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.
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.
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.
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.
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.
Kok, T. (2026). KELIX Enterprise AI Platform [Case study]. terencekok.com. https://terencekok.com/projects/kelix-enterprise-ai-platform/
The same diagnostic logic from deployments like this one, applied to your business in half a day.
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The hardest part was convincing engineers who'd spent twenty years trusting a shared drive over a chatbot that this one wouldn't hallucinate their way into a compliance breach on a multi-billion dollar site. Security reviewed every layer before a single query went live, because there's no room for 'probably fine' at that scale. What stays with me is watching a site engineer in Riyadh pull up a spec in seconds that used to cost him an afternoon of phone calls. That's the platform doing its job, which is exactly how it should be.
