Design and deploy the environment behind your software. Connect its data and AI services, or migrate a workload to AWS or Azure, with a clear plan for how it will run.
Start with a new application, an AI feature or an environment that needs to change. We define the technical work around that need.
Turn application requirements into an environment that fits the workload, its data and the people who will operate it.
Bring the application, model connection and supporting data components together so AI can run within useful software.
Move approved information between sources and applications in the format and sequence the process needs.
Assess the system you run today, plan the agreed change and focus improvements on the workload that matters.
Infrastructure has a purpose. It should give the application the data, services and operating boundaries it needs.
An internal knowledge application connects approved documents to a retrieval layer, an application and a model service. Your team defines who can access the information and where the workload will run.
A scoped deployment brings those components together, checks the agreed release and data paths, and records configuration and operating responsibilities.
Review the current workload and its dependencies before planning a move to the agreed AWS or Azure environment.
Bring business sources into a defined processing pipeline, with access and exception handling established before release.
The platform choice follows the environment you already run, where your data sits and what the application needs. We explain the recommendation and scope the services around it.
Design and deploy in an agreed AWS account, with the application services, data connections and access required for the workload.
Design and deploy in an agreed Azure environment, with application, data and AI services connected to the way your organization works.
Architecture starts with the workload, data, access and operating model. Account ownership and ongoing responsibilities are agreed separately from the platform choice.
The outputs follow the work commissioned. An assessment can stand alone; a scoped implementation carries the agreed environment through release and handover.
Understand the workload, dependencies and constraints. Record findings and agree the work to be done.
Set out the architecture and deployment approach. Build, connect or migrate the components included in the proposal.
Check the agreed release and data paths. Record configuration, access, operating roles and the next actions.
Discovery establishes the fit. Further investigation is a separately scoped, paid engagement where needed. The proposal separates implementation, infrastructure usage and optional support.
How we workThe right deployment arrangement depends on your application and how your team wants to operate it. We agree account access, data handling and responsibilities before deployment.
Your organization owns the AWS or Azure account and its usage charges. Kastling works with the access required for the agreed delivery.
The application runs in Kastling’s accounts, with usage billed on a pay-as-you-go basis and account administration defined in the proposal.
Monitoring, backups, updates and incident response are included only where explicitly scoped. Hosting and ongoing support are separate decisions.
Yes. Cloud architecture, AI application deployment, data connections and the migration of agreed workloads can be commissioned independently, or as the foundation for a wider project.
The workload, architecture, dependencies, access constraints and the reason for changing it. We agree the review scope and the information needed before access is provided.
Model usage, data volumes, storage and the services the application depends on. These operating costs are considered separately from the implementation and any maintenance agreement.
Review the infrastructure, ownership and operating requirements your workload will need.