Independent · NVIDIA / Intel / AMD
Ai infrastructure
that actually ships.
Embedica designs, procures, and deploys the GPU compute, networking, and storage enterprise AI workloads need — vendor-agnostic advice, hardware that’s actually in stock, and infrastructure built for where the next rack generation is headed, not just this one.
IntelXeon, Gaudi, oneAPI
AMDEPYC, Instinct, ROCm
Where the market is headed
Power and cooling are the new bottleneck — not the chip
Typical AI rack power density in a few hardware generations — liquid cooling has gone from optional to default.
Site power availability, not GPU allocation, is increasingly what decides where AI infrastructure gets built.
Regional AI infrastructure and edge inference are growing alongside centralized training — most enterprises will need both.
What we deliver
Four services — no sub-menu of thirty things nobody clicks into.
Design & consultation
Workload-first sizing across GPU, CPU, network, and power/cooling — vendor-agnostic, tied to your budget and timeline.
Procurement & build
Sourcing, installation, and integration — from a single GPU server to a full-facility AI factory build-out.
AI software & platforms
NVIDIA AI Enterprise, CUDA, Intel oneAPI, AMD ROCm — deployed and integrated so hardware turns into working AI.
Support & optimization
Ongoing tuning, monitoring, and lifecycle support — a retainer, not a one-time install.
How an engagement runs
Four stages, start to steady-state.
Workload & budget
A paid assessment fixes the workload profile, power envelope, and realistic budget before any hardware is specified.
Architecture
Compute, fabric, storage, and power/cooling specified against three vendor platforms, not one catalog.
Build & integrate
Procurement, installation, and software stack integration, tracked against the lead times quoted up front.
Support retainer
Monitoring and performance tuning after go-live, backed by direct manufacturer and partner relationships.
Start here
Infrastructure readiness assessment
The fixed-fee first step, for teams who need a real number before they go to procurement or the board.
- Workload profiling across your current and planned training/inference jobs
- A vendor-agnostic build spec — GPU, network, storage, power & cooling
- Two sized options with hardware lead times and total landed cost
- A written report you can take straight into procurement
Fixed fee
Credited in full against any build or deployment engagement that follows.
Recent builds
Illustrative examples — swap in your own deployments and figures.
