Production AI and GenAI Engineering on AWS

CodetoKloud builds focused AI applications such as internal knowledge assistants, document processing workflows, classification and extraction services, and AI features within existing software. We start with a defined business workflow and only recommend AI when the data, quality threshold, and operating model support it.

We compare suitable models using representative business examples, required accuracy, latency, privacy, integration effort, and operating cost. The decision is based on measured fit for the use case rather than a preferred vendor or the largest available model.

Yes, when the selected architecture and model option support that requirement. We can keep application data, retrieval indexes, logs, and supporting services in your AWS account, apply least-privilege access, and document any external model endpoints that remain in the data path.

A production AI system needs repeatable evaluations, access controls, output guardrails, monitoring, cost limits, failure handling, deployment automation, and an owner for ongoing review. We include those operating requirements in the design instead of treating the model response as the finished product.

A useful first review needs the current workflow, intended users, representative inputs and outputs, relevant data sources, privacy constraints, integration points, and a measurable definition of success. We can help refine those inputs during discovery.