Select the right combination of edge, on-premise, hybrid, and cloud infrastructure for your AI system. Run AI where performance, control, and operations require it — not where a vendor's default architecture puts it.
Most deployment architectures are inherited from whichever platform the first pilot happened to use.
The system must respond within a fixed latency budget, or keep operating when connectivity drops — on machines, vehicles, remote sites, or in orbit. A cloud-only architecture cannot meet those constraints.
Sensitive data may not leave the site, the device, or the jurisdiction. The organization must retain architectural control — over data location, security boundaries, and its dependence on individual providers.
Inference costs, data transfer, and hardware lifecycles behave very differently at production scale than in a pilot. Without a deliberate deployment architecture, the operating model breaks exactly when the system succeeds.
Does inference need to happen locally?
What latency is acceptable?
Can the system operate without connectivity?
Which data may leave the site or device?
How much compute is required?
Which hardware is appropriate?
How will models be distributed and updated?
What happens when the model or infrastructure fails?
What is the expected operating cost?
How will the system be monitored?
How much architectural control must the organization retain?
We evaluate the options systematically: each candidate architecture is scored against the dimensions that actually decide whether the system performs, stays under control, and remains affordable in operation.
The result is a defensible decision — with the trade-offs made explicit before hardware is bought or contracts are signed.
Edge, on-premise, hybrid, and cloud options scored against your constraints — with the trade-offs made explicit.
The selected architecture documented end to end: components, environments, and how they interact.
Concrete hardware recommendations and compute sizing for training, inference, and growth.
Where data is captured, processed, stored, and retained — and which data may cross which boundary.
Defined security zones, access boundaries, and the controls each environment must enforce.
How models are distributed, versioned, and updated across devices, sites, and environments.
Defined behavior when the model, the connection, or the infrastructure fails — degradation instead of outage.
Operating-cost scenarios across architecture options and load levels, so the economics are visible before commitment.
A staged rollout recommendation: what to deploy first, what to validate, and when to scale.
How the engagement works
Four phases, each closing with a decision or output — from constraints to a rollout plan you can commit to.
We capture the constraints that decide the architecture: latency, connectivity, data sensitivity, sovereignty, compute demand, and operational realities.
Candidate edge, on-premise, hybrid, and cloud architectures — each evaluated across the fourteen dimensions against your constraints.
The deployment decision matrix, hardware and compute sizing, data-location concept, security boundaries, and cost scenarios.
The target architecture, model deployment and update process, availability and fallback concept, and a staged rollout recommendation.
Often more so. The question is rarely cloud versus no cloud — it is which workloads belong there and which must run at the edge, on-premise, or in a hybrid setup because of latency, connectivity, data control, or cost. We design the combination, including how it fits your existing provider commitments.
We are independent. We do not resell hardware, cloud capacity, or platform licenses. We assess technologies, infrastructure, and hardware across the complete system context and bring in specialized domain expertise where required — the recommendation is built on your constraints, not on a margin.
Data sensitivity and sovereignty are explicit dimensions in the decision matrix, not afterthoughts. The data-location concept defines what may leave which site, device, or jurisdiction, and the architecture defines how much control your organization retains over infrastructure and providers.
Before hardware is bought and before infrastructure contracts are signed — ideally when the system architecture is taking shape. It also works as a corrective: if an existing deployment is too slow, too expensive, or too dependent on one provider, the same analysis produces a migration path.
Translate strategic intent into requirements, system boundaries, and technical decisions.
Build and validate the technically critical parts of advanced AI products.
Decide what to build, what to buy, and where a combined approach wins.
Whether you are designing a new system or questioning an existing one — we will show you where your AI should run, and what it will cost to run it there.
Review Your Deployment Architecture
