AWS for the parts that need it.

Very few of our projects are pure AWS, and very few are pure edge. The architecture that actually holds up is a hybrid: the front end and API routes deploy on an edge platform for speed and simplicity, while AWS handles the specific jobs an edge runtime cannot. Object storage on S3 keeps large files and sensitive documents out of the application path, served through presigned URLs so nothing streams through our servers. Durable compute on EC2 runs the long processes and persistent connections an edge function times out on, with Lambda handling event-driven and scheduled jobs. We draw the boundary deliberately, so each workload sits on the platform built for it and the client gets enterprise-grade capability without an enterprise-grade operations burden. This is the same discipline we apply to hosting decisions everywhere: use AWS where it is the right tool, not as a reflex, and keep the two platforms cleanly separated.

When a build needs real AI capability, we reach for AWS Bedrock rather than managing GPU infrastructure ourselves. Bedrock gives us foundation-model access for text generation, summarization, and intelligent document processing behind a single managed API, which keeps model choice flexible as the ecosystem shifts. We pair it with S3 for the document side: uploads land in storage through presigned URLs, get processed by a pipeline, and return structured results the application can act on, all without sensitive files ever touching the front end. For clients in regulated spaces this separation matters, because the data stays inside AWS with access scoped tightly rather than passing through every layer of the stack. The result is production AI features (search over private documents, generated summaries, automated intake) running on infrastructure a client's compliance team can reason about, without us standing up and babysitting model servers.