Cloud Platforms & AI Engineering

Give developers one standard route from code to production. They request a new service or environment through a self-service portal and receive it set up the approved way, without waiting for an infrastructure ticket. Every change is recorded in Git and rolled out automatically. A new version goes to a small share of users first and continues only if its health checks pass. You can see how often you release, how long a change takes to reach production, how often a release fails and how fast you recover.

AI is part of the route. We draft infrastructure code and service templates with AI coding tools, and an engineer reviews each change. Models are trained and released through the same steps as any other service.

Serve many customers from one platform and keep each customer’s data separate, including the information your AI draws on for that customer. AI agents use computing power in bursts, so we set limits that keep one customer’s heavy use from slowing the others. Customers who need stricter separation can have dedicated resources. Adding a customer follows the same automated steps, whether it is your first or your hundredth. You see health and cost for each customer, which shows what each one costs to serve.

Run open-source language models on GPU servers inside your own AWS account. Capacity follows demand, which limits what you spend on idle GPUs, and you can see response times and GPU use. To make a model fit your business, we first connect it to your approved documents, so answers use current company information and can point to their source. When a model needs a specialized skill or a consistent style, we fine-tune it on your examples and compare its answers with the original’s before you switch.

Not every product needs its own model. The platform can also call models through Amazon Bedrock, AWS’s managed model service.

A promising agent demo leaves three questions open. What is the agent allowed to do? Who is it acting for? Where does the code it writes run? We build the answers into the platform.

Agents reach AI models through a gateway, so they never hold the access keys. They reach tools through a second gateway, which checks each request against what the current user is permitted to do. Every prompt, tool call and cost is traced, so you can review what an agent did and what it spent. Code that an agent writes runs in an isolated sandbox with no network access, away from your other systems and data. Coding agents your developers use can run on the same platform.