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LLM features and workflow automation built as backend systems: queues, retries, cost control and fallbacks.
An LLM feature is mostly a backend problem. The model call is a few lines; the engineering is everywhere else: queueing work so a slow provider does not block a request, retries and idempotency, timeouts, token and cost limits, evaluation of the output, and a fallback for when the provider is down or wrong. That is ordinary hard backend engineering, and it is the work we do: Step Functions, Lambda, SQS and third-party APIs under production conditions.
The manual task, its volume, its cost, and what a wrong answer would actually break.
Events, queues, state machine, retries, review points and the fallback when the model fails.
A test set from your real cases and criteria you agree with, before any rollout.
Alarms on error rate, latency and spend, plus the runbook for switching it off.
Not as a portfolio of AI case studies, and we will not pretend otherwise. What we have built repeatedly is the system such a feature lives in: event pipelines, queues, retries and third-party APIs under load. That is the part that usually fails.
Whichever fits the task, the budget and the data constraints, reached through Bedrock or a provider API. The design keeps that choice revisitable without touching the rest of the system.
Decided before anything is built: what leaves your infrastructure, which region processes it, what is retained and what is logged. If nothing may leave, that narrows the options and we say so early.
With a test set built from your real cases, scored on criteria you agree with and re-run on every change. Without that, quality is an opinion.
Tell us what you are working on. Reply within 24 hours, one-page diagnostic within 48.