A fully automated AI processing pipeline on serverless AWS. Event-driven, multi-step, monitored in real time, and running in production with zero manual intervention.
A multi-step processing workflow needed AI in the loop, but the economics had to make sense. A standing service footprint with idle compute, or a pipeline that needed a human to babysit it, would have eaten the value the automation created.
The bar was simple: the pipeline runs itself, costs scale with actual usage, and someone finds out immediately when something goes wrong. Not from a user.
Serverless compute throughout, so there is no idle infrastructure to pay for. When nothing is processing, the pipeline costs almost nothing.
Workflow orchestration coordinates multi-step processing across integrations. Each step is retryable and observable, so a failure in one stage never silently corrupts the run.
Inference through managed AI services rather than self-hosted models. No GPU fleet to operate, and the AI layer scales with the same usage-based economics as the rest of the stack.
Real-time monitoring and alerting built in from the first deployment, not bolted on after the first incident. State tracked in NoSQL storage so every run is auditable.
The pipeline is fully operational at under $35 per month, with zero manual intervention required. That number is the point: production AI doesn't have to mean a production-sized bill. Architecture decisions, not optimization heroics, are what keep it that cheap.
This is the same architectural discipline behind Skyform's AI Build engagements and behind Ridgeline, Skyform's own AI advisor. Guardrails, observability, and cost controls are part of the design, not a phase two.
Client details are anonymized under confidentiality agreements. References available on request.
An AI Build takes it to production in 6 to 10 weeks: agent, pipeline, or integration, with guardrails and cost controls included and 30 days of post-launch support. Public pricing, fixed scope.