Connect AI data center assessment, feasibility and solution architecture. Evaluate GPU capacity, inference performance, memory, power and cost with explicit evidence gates and a scoped post-quantum readiness workstream.
Review cryptographic inventory evidence, data confidentiality lifetimes, platform dependencies and migration schedules. Build a scoped post-quantum readiness and crypto-agility plan with explicit interoperability, performance and recovery gates.
Connect AI infrastructure and post-quantum readiness assessments to future-state requirements, evidence gates and a conditional solution architecture. Review capacity, quality, latency, cost, resource and migration constraints before committing to implementation.
Agree the operating problem, business objects, responsibilities, and evidence needed for the receiving outcome. Preserve the ownership of enterprise and operational records.
Start with one bounded workflow and a client-confirmed baseline. The proposal defines integrations, acceptance, delivery responsibilities, and the next stage of scope.