Cloud · AWS · Adopt
Practical Generative AI on AWS, From First Experiment to Production
Most businesses know AI matters but aren't sure how to move past a proof of concept. We help identify high-value use cases and build production-grade generative AI solutions on AWS's AI infrastructure, including Amazon Bedrock.
AWS Has the AI Infrastructure — We Build What Runs On It Reliably
AWS provides serious infrastructure for generative AI — Amazon Bedrock's access to foundation models, SageMaker for custom model work, and the compute infrastructure to serve it all reliably. But infrastructure alone doesn't get you from a demo to a production system people depend on. That gap requires solid use case discovery, integration with your actual data and workflows, and the monitoring and governance a live system needs.
We approach AWS-based generative AI the way we approach any infrastructure decision: start with the business problem, then build the solution — including the AWS infrastructure to run it reliably — around that.
What Generative AI on AWS Covers
Use Case Discovery
Identifying high-value, technically feasible use cases and assessing data readiness
Solution Design
Architecture spanning Amazon Bedrock, SageMaker, or fine-tuned models depending on the use case
Integration
Connecting AI solutions to your existing applications and data sources
Deployment Infrastructure
Scalable, secure infrastructure for hosting and serving models on AWS
Data Pipeline & Governance
Reliable data pipelines and access controls supporting the AI system long-term
Who This Is For
- Businesses with a clear problem but unsure whether or how generative AI applies
- Teams with a proof of concept that never made it to production
- Organizations with data on AWS that isn't yet organized to support AI reliably
- Companies wanting to move on AI without taking on unmanaged compliance or reliability risk
Common Questions
Do you build custom models, or use Amazon Bedrock's foundation models?
Most engagements use Bedrock's foundation models via API or fine-tuning, since building a model from scratch is rarely justified. We recommend the right approach based on your specific requirements.
We don't have clean or well-organized data — can we still start?
Data readiness is usually the first thing we assess honestly. Some use cases can proceed with data remediation as part of the project; others may need groundwork first.
How do you handle the risk of AI generating incorrect output?
Through retrieval-grounded design, evaluation frameworks, and human-in-the-loop review for higher-stakes use cases, depending on how the output is used.