AI Infrastructure & Readiness

Most organizations approach AI backwards: they pick a model or a vendor before they know whether their infrastructure, their data, or their governance can support it. That order produces expensive pilots that never make it to production, usually because nobody checked the compute budget, the data was never cleaned up enough to trust, or legal had a problem with where the data was going that surfaced after the contract was signed.

We start with the infrastructure question instead: what would actually running this cost, in compute and in risk, and does it belong on your hardware or somebody else's. Some workloads make sense on public AI platforms. Others — anything touching regulated data, anything a customer would not want leaving the building — make more sense run locally, on infrastructure sized for the job rather than for a vendor's roadmap.

The governance side matters as much as the hardware. Model access, data retention, and an acceptable use policy your staff can actually follow are not optional add-ons; they are usually the difference between a pilot that survives contact with legal and one that does not.

What the engagement covers

• An assessment of which workloads justify AI infrastructure and which do not, yet
• Compute sizing for on-premises or hybrid inference, based on real workloads rather than vendor benchmarks
• Data readiness — the cleanup and governance a model needs before its output can be trusted
• An acceptable use and data handling policy your team can actually operate under

What doing it in order gets you

Infrastructure first, model second, in that order, is what turns a pilot into something that survives to production.