Requirements and sizing
We map use cases, model options, expected load, latency goals, data flow and the existing environment.
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KAYRO / SERVICES / 05
AI systems & infrastructure
An architecture for the workload, data and operating environment.
Discuss your project01 / THE CONTEXT
AI needs an operating environment, not only a model
Private inference, document retrieval and agent workflows depend on data access, latency, compute, security and people who will operate the system. We assess these conditions before choosing hardware.
- A private data boundary or local deployment is required.
- The workload is unclear: users, context size, concurrency and growth.
- A team needs a verifiable plan for installation and operation.
02 / WHAT WE DO01 — 04
A complete project with clear responsibilities.
We assess private AI use cases, model workloads, data paths and compute options before defining an installation. Hardware, deployment and ongoing operations are specified in a project proposal.
Architecture options
We compare on premises, cloud or hybrid approaches and identify compute, memory, storage and network constraints.
Deployment and integration
Where agreed, we connect model serving, retrieval, applications, identity and observability in a defined environment.
Acceptance and handover
We agree checks for functionality, access, quality and operating load, then document ownership and support boundaries.
03 / KEY DECISIONS
The decisions that shape the project.
Scope follows the real context. We clarify these points before committing time to development.
Data and access
What may leave the organisation, who can use each source and how activity is logged.
Capacity and expansion
The representative workload, growth assumptions and the limit at which the design must change.
Responsibility
Who supplies equipment, hosts services, applies updates and responds to faults.
04 / FROM PLAN TO RELEASE
A process we can verify.
We begin with workloads, expected use, data boundaries and site constraints; then compare feasible configurations and define a verifiable deployment scope.
Areas we can include
- Requirements and sizing
- Compute architecture
- Private AI deployment
- Data and access design
- Acceptance tests and handover
05 / BEFORE WE START
Common questions.
Can you build a private AI service for our company?
We can assess and scope a private setup. The suitable deployment and installation responsibilities follow the workload, data rules and available environment.
Do you have a fixed hardware package?
No. A configuration is proposed only after sizing and compatibility review; availability and terms are confirmed in the specific offer.