Your AI prototype works in a notebook. It doesn't belong in production.
Models deployed without guardrails, no cost controls, no latency SLAs, no evaluation framework. The gap between a working demo and a production AI system is where most teams get stuck.
Where most teams get stuck
Models deployed without guardrails, no cost controls, no latency SLAs, no evaluation framework. The gap between a working demo and a production AI system is where most teams get stuck.
Production AI Architecture
Bedrock, SageMaker, and open-source models on your own VPC. RAG pipelines, evals, and production deployment patterns — for workloads that have to earn their compute.
What you'll have when we're done
AI architecture design & model selection
Framework for choosing between proprietary APIs, managed services, and self-hosted models.
RAG pipeline with vector store & retrieval
Retrieval-Augmented Generation setup with OpenSearch and retrieval optimization.
SageMaker or Bedrock deployment with VPC isolation
Production-ready inference endpoints with cost controls and latency monitoring.
Evaluation framework & latency/cost benchmarks
Automated testing, performance monitoring, and cost-per-query tracking.
Monitoring & drift detection setup
Model performance tracking, data drift detection, and automated retraining triggers.