Bedrock-backed GenAI applications
LLM applications built on Amazon Bedrock — model selection, prompt and context design, and the cost controls that keep them affordable at production volume.
What we’ve built
Systems built and run on AWS
The advice comes from the build. Below is the kind of production AWS work behind the practice — described generically; no client or employer is named.
CloudCraft’s perspective on GenAI architecture and cost comes from having designed, shipped, and operated these systems — not from reading about them. The areas below are the building blocks of the LLM and RAG systems CloudCraft designs and reviews today.
Building blocks
RAG pipelines
Retrieval-augmented generation end to end: ingestion, chunking, embeddings, and retrieval tuned for answer quality without runaway retrieval cost.
Serverless backends
Production backends on Lambda, API Gateway, DynamoDB, and Aurora — built to scale to zero when idle and hold up under real load when not.
Multi-account infrastructure as code
Multi-account AWS environments defined in CDK, with the isolation and promotion paths that let systems move from dev to production safely.
Production engineering
The work that separates a demo from a system: cache isolation, rate limiting, and promotion workflows — the parts that never show up in the architecture diagram.
At scale: a public-filings ingestion pipeline
One representative build is an automated pipeline that ingests SEC EDGAR XBRL filings across thousands of public companies — a system, not a script. It includes the production engineering that operating at that scale demands: cache isolation so workloads don’t collide, rate limiting that respects upstream limits without stalling throughput, and promotion workflows that move changes through environments safely. It is the kind of unglamorous, reliability-defining work that decides whether a data system runs for years or breaks the first time volume spikes.
Want this kind of build behind your GenAI system?
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