AWS Generative AI Competency
From strategy to production: use-case prioritization, Amazon Bedrock and SageMaker architecture, evaluation, governance, and cost control before scaling.
Before architecture
Use case fit
Is GenAI actually the right path?
Is this workflow a good candidate for generative AI, or would search, rules, automation, or analytics solve it more reliably?
Data readiness
Current and permissioned sources
Are the source documents current, permissioned, clean, and owned by someone who can keep them accurate?
Risk boundary
What happens when the AI is wrong?
Does the workflow need review, approval, rollback, human-in-the-loop control, or hard limits for sensitive actions?
Path to production
Launch criteria
Before launch, define the evaluation set, logging, cost model, security review, fallback behavior, owner, and operating playbook.
The challenge
Generative AI promises to transform operations, but most initiatives get stuck in POCs that never reach production. The challenges: identifying use cases with real ROI, integrating with enterprise data, controlling inference costs, and ensuring governance. An end-to-end strategy connects business vision to technical implementation.
Who it's for
CTOs, VPs of Engineering, and data leaders who need a clear generative AI strategy. Companies that have already done POCs and want to scale, or those just starting and wanting to avoid common pitfalls.
Use-case selection
The first decision is not the model. It is which process deserves to change, how success will be measured, and who operates it afterward.
From POC to production
GenAI services
Workshop to score use cases by business value, data readiness, feasibility, risk, evaluation clarity, cost predictability, and ownership.
RAG with Knowledge Bases, agents, guardrails, and enterprise data integration, with permissions, evaluation, cost per task, and operations defined early.
Training, fine-tuning, and deployment of custom models with SageMaker. For cases where foundation models don't suffice or proprietary data requires dedicated models.
Monitoring for quality, groundedness, tool-call accuracy, safe failure, latency, unit cost, prompt/model review, and runbooks.
35%
reduction in a documented inference case
GenAI
AWS Generative AI Competency
250+
launches on AWS
Data and AI ecosystem
Related guides
Technical implementation details with Bedrock.
Strategy, RAG, agents, and governance for Canadian teams.
Workshops and GenAI implementation for Toronto/GTA teams.
GenAI on AWS with Brazilian context, LGPD, and the São Paulo Region.
Generative AI for São Paulo companies with AWS workloads.
Control unit cost for Bedrock, SageMaker, and RAG.
Connect models to data, tools, and auditable workflows.
The data foundation that feeds your AI models.
Control inference costs and AI infrastructure spending.
About Elevata
Elevata is a consulting company specialized in helping your business tap into the full potential of AWS. Whether it's generative AI, modernization, or migration, our solutions are built to support efficient, sustainable growth. As an AI-native AWS Advanced Partner, we bring deep AWS expertise to help you adopt generative AI and build secure, scalable cloud environments aligned with your business needs and focused on outcomes you can sustain and build on over time.
More about usFrequently asked questions
Amazon Bedrock is ideal for using foundation models (Claude, Llama, Titan) via API without managing infrastructure. SageMaker is for training and deploying custom models when foundation models don't suffice. Most projects start with Bedrock and use SageMaker for specific needs.
The AWS Generative AI Competency is awarded to partners who demonstrate technical proficiency, proven customer experience, and success stories in generative AI. Elevata went through technical validation and case review by AWS.
A POC with Bedrock can be delivered in 2-4 weeks. A full implementation with RAG, agents, and data integration takes 6-12 weeks. Initial strategy and discovery takes 1-2 weeks.
Yes. As an AWS Partner, we help assess eligibility for programs such as PoC and AWS Activate. Credit availability depends on the use case, company stage, and AWS approval.
Next step
Schedule a discovery workshop to identify the generative AI use cases with the highest impact for your company.