Strategic capabilities built to get enterprise infrastructure ready for AI transformation — and to keep it that way under real production load.
The layer most AI vendors skip — and the one that decides whether your AI initiative ever reaches production. A structured audit and rebuild of the data, compute, and governance foundation your AI transformation actually depends on.
Leveraging experience delivering over €500K in annual savings at enterprise scale through rightsizing, commitment optimization, and infrastructure governance — the cost discipline AI workloads make non-negotiable.
Integrating production-ready LLMs, intelligent monitoring, and automated pipelines to remove operational bottlenecks and speed up service delivery once the underlying infrastructure can support them.
Most vendors show you the top of the stack — the chatbot, the copilot, the dashboard. That layer only works if the three underneath it are built to carry it. InTechgrale starts at the bottom: cloud and compute, then data and pipelines, then governance and security — so the AI layer you announce to the board is standing on something solid.
We don't sell you the demo. We build what the demo needs to still be working in twelve months.
of enterprise AI projects fail to deliver their intended business value — roughly twice the failure rate of standard IT projects. Source: RAND Corporation, 2024
of enterprise generative AI pilots deliver zero measurable financial return. Source: MIT Project NANDA, 2025
of AI projects lacking AI-ready data infrastructure are projected to be abandoned through 2026. Source: Gartner
The common root cause isn't the model, the vendor, or the talent — it's infrastructure that was never rebuilt to carry AI workloads: fragmented data, ungoverned cloud spend, brittle pipelines, no operational monitoring for what happens after go-live. RAND's research names it directly: inadequate infrastructure and poor data readiness are leading causes of AI project failure, ahead of the model itself.
Take the 5-minute assessment first — it points to the weakest layer before you commit budget.
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