A platform the AI can run on
Landing zone, accounts, networking, and environments engineered so an AI workload has a dependable place to run, not a pilot bolted onto a laptop.
AWS provides the AI building blocks. KineticSkunk's Cloud Platform Engineers provide and operate the secure, observable, cost-aware AWS platform your AI workload runs on in production.
Built for South African teams moving an AI pilot into dependable production use on AWS.
Your team owns the model, the prompts, and the application. We engineer and operate the AWS platform underneath it, so the workload can be secured, observed, scaled, recovered, and run.
Landing zone, accounts, networking, and environments engineered so an AI workload has a dependable place to run, not a pilot bolted onto a laptop.
Identity-first access, secrets handling, network paths, and data-residency decisions defined before the workload is exposed to customers or teams.
Signals across platform health, latency, errors, usage, and cost, connected to alerts and responders so issues surface before users report them.
Delivery, rollback, recovery, cost control, and ownership set up as an operating rhythm your team can run, or that we run with you.
AI pilots prove an idea can work. Production asks a different question: can the workload be secured, released, observed, scaled, recovered, and operated when customers and teams depend on it? Those are platform questions.
A useful demonstration proves the idea. It does not give the workload a secured, observable, recoverable place to run when the business depends on it.
Broad permissions, copied data, and convenient integrations are fine while exploring. Production needs deliberate access, secrets, logging, and regional decisions.
Inference, retrieval, storage, and supporting services meet real demand for the first time in production, and the platform has to hold up and stay affordable.
The team can build the model, but monitoring, incidents, recovery, and change to the AWS platform around it often have no clear owner.
We use AWS-native services and platform-engineering discipline to give the AI workload a place to run that your team can operate and explain, the same way we run any AWS platform.
Accounts, environments, networking, and guardrails shaped around how the workload runs and how your team operates.
Least-privilege identities, secrets handling, network paths, endpoint controls, and the access boundaries the model and its users depend on.
Classification, encryption, retention, logging, and residency decisions for the data the workload receives, retrieves, generates, and stores.
Platform, usage, latency, error, and cost signals connected to dashboards, alerts, runbooks, and accountable responders.
Repeatable deployment, rollback, backup, and restore for the infrastructure and configuration the workload runs on.
Cost visibility, incident response, change control, and improvement run as ongoing operations, not a one-off setup.
Expand each block to review what we engineer, fit signals, platform outcomes, standalone or managed platform paths, and the staged delivery approach.
The work is scoped around the platform the workload needs next, not around unnecessary infrastructure.
Account structure, environments, networking, and baseline guardrails that give the AI workload a dependable place to run on AWS.
Least-privilege roles, secrets handling, and access boundaries for the model endpoints, data, and tools the workload depends on.
Private network paths, endpoint controls, encryption, retention, and regional decisions for the data the workload uses.
Metrics, logs, and traces across platform health, latency, usage, and cost, connected to alerts and runbooks operators can act on.
Repeatable infrastructure delivery, tested rollback, and backup and restore for the platform and configuration the workload runs on.
Cost drivers, budgets, and usage measures made visible so inference, retrieval, storage, and supporting services stay affordable at scale.
If several of the signals below reflect your reality, engineering the AWS platform under your AI workload may be a practical next conversation.
The use case works, and now it needs a secured, observable AWS platform to run on before real users depend on it.
Customer, employee, financial, or operational data needs clear access, encryption, retention, and residency boundaries on AWS.
Pilot usage has not shown how inference, retrieval, storage, and supporting services will behave and cost under production demand.
Monitoring, incidents, recovery, and change to the AWS platform around the workload need a clear operating owner.
These outcomes are what the work is designed to deliver: a secured, observable, recoverable, cost-aware AWS platform your AI workload can run on.
Accounts, network, identity, secrets, and data boundaries engineered around how the AI workload runs.
Health, usage, latency, error, and cost signals connected to alerts, runbooks, and accountable responders.
Repeatable delivery, tested rollback, and backup and restore for the infrastructure the workload depends on.
A defined operating model for the platform under the AI, whether your team runs it or we run it with you.
AWS AI Cloud Infrastructure can give one AI workload a dependable platform as a focused piece of work, or become part of AWS Managed Platform when the platform under the AI needs ongoing ownership.
Use this when the immediate need is to give one AI workload a secured, observable AWS platform to run on.
Use this when the platform under the AI needs ongoing ownership: monitoring, incidents, cost, recovery, and change.
Explore AWS Managed PlatformPair AI platform work with access governance when stakeholders need to understand who and what can reach the workload.
Explore Zero Trust Security
The work is practical and scoped: engineer the AWS platform the workload runs on, then operate and improve it.
We start with the AI workload, its intended production use, the data it touches, and the platform decision your team needs to make.
We review accounts, networking, identity, data paths, controls, observability, cost, and recovery around the workload.
We define the landing zone, security and data boundaries, observability, delivery, recovery, and cost model that fit the workload.
We build the platform, close priority gaps, wire the signals and controls, and validate that the workload runs the way it should.
The platform runs through an operating rhythm: monitoring, incidents, cost, recovery, change, and improvement, with your team or ours.
The value is not enabling AWS services. It is shaping them into a platform your team can run, observe, recover, and afford, while your team owns the model itself.
Operate secured, logged access to managed foundation models where Bedrock fits the workload, region, and data path.
Run the application, orchestration, retrieval, and integration components on the container platform that fits your team.
Operate event, API, and integration paths for the workload with controlled scaling and access.
Create operational and audit visibility across workload health, activity, latency, errors, and the signals support and review need.
Establish least-privilege identity, access boundaries, encryption, and key management for the workload and its data.
Shape network paths, endpoints, segmentation, and threat detection appropriate to the workload and its exposure.
Protect the configuration, data, and storage the workload depends on, with backup coverage and recovery paths.
Support configuration visibility, guardrails, and cross-account governance across the AWS environments in scope.
Engineering the AWS platform under an AI workload can stand alone as a focused piece of work. Where monitoring, cost, recovery, security, and change need to continue, the agreed platform scope can become part of AWS Managed Platform operations.
Tell us about the workload, the data it touches, and the production decision in front of your team. We will help you shape the AWS platform it needs, and operate it with you when ongoing ownership adds value.
Optional platform check
Take a 10-question check of the AWS platform around one workload, and see which areas are in good shape and which need a closer look. You get the result immediately, with no AWS access or contact details required.
This is a directional self-check based on your answers. It does not certify that a workload is ready for production.
