GenAI Innovation Lab & Data Services

Steamhaus delivers the platform engineering rigor required to run Data and AI workloads in production. We deploy secure, multi-account landing zones specifically optimised for data isolation and high-throughput processing. We establish robust MLOps pipelines using Amazon SageMaker Projects, integrating CI/CD workflows for automated model retraining, versioning, and endpoint management. This eliminates manual infrastructure provisioning and ensures consistent, repeatable deployments across development and production environments.

We architect Modern Data Stacks to support high-performance ingestion and retrieval. This involves building scalable ETL workflows with AWS Glue, enabling serverless interactive analysis via Amazon Athena, and enforcing strict governance through Amazon DataZone. A core component of our delivery is Vector Engineering. We configure Amazon OpenSearch Serverless or Amazon Aurora PostgreSQL as optimised vector stores to underpin Retrieval-Augmented Generation (RAG) architectures.

For Generative AI integration, we implement secure application layers that bridge private data with Foundation Models. We build managed API gateways to facilitate governed access to Amazon Bedrock. This creates private, monitored interfaces for internal applications. We ensure that all LLM interactions utilise private networking and adhere to enterprise IAM policies rather than exposing public endpoints to the internet.

Key outcomes

This service provides a production-grade data foundation to rapid-start your AI and analytics initiatives, unlocking benefits such as:

• Production-Ready Infrastructure: Deploy a secure, multi-account baseline with pre-configured networking and IAM guardrails for data workloads.

• Automated MLOps: Standardise model lifecycles with Amazon SageMaker Projects to ensure full traceability from training to inference.

• Governed Data Access: Implement centralised data governance and lineage using Amazon DataZone and AWS Lake Formation.

• Scalable RAG Architecture: Integrate Amazon Bedrock with production-grade vector stores to deliver low-latency, context-aware AI responses.