Questions to ask an AWS AI implementation partner
Ask for evidence that connects the proposed architecture to your workflow: relevant delivery experience, measurable evaluation, integration boundaries and an explicit operating owner.
What do the credentials actually cover?
Check the exact programme, category and year. Partner tier, a technical competency and an annual award describe different things. Use the official partner profile and the source announcement to verify what a supplier claims.
CloudHero is an AWS Advanced Tier Services Partner. Our AI Services Competency announcement names Generative AI Consulting Services. AWS named CloudHero the 2024 Global Application Modernization SI Partner of the Year. The modernization award should not be described as an AI award.
Credentials are useful evidence of a supplier’s practice. The proposed team, responsibilities and scope still need to fit your project.
Can they show a complete delivery workflow?
Ask what the application reads, which systems it integrates with, what it can change and how a person reviews consequential actions. Request examples of failure handling and handover, alongside the successful demonstration.
Our Mindory / AutiHD case study describes a production AI layer using Amazon Bedrock AgentCore, persistent memory, controlled tools and tracing. Octora’s Otto assistant is an owned-product example of evidence retrieval, proposed actions and validated execution. These are different types of evidence: a customer implementation and CloudHero’s own product engineering.
A demonstration should say which connections are real, which are simulated and which steps need additional implementation. Ask the delivery team to identify these boundaries before treating a demonstration as production evidence.
How will they prove that a change is better?
Ask for a baseline, representative test cases, a method for reviewing ambiguous results and criteria for release. Clarify whether a reported metric measures the model, a category of tasks or the whole application.
In the RaptorCI evaluation, overall recall and precision are reported separately, with the size and scope of the test set. Infrastructure error rates and model spend have different meanings. Request the same clarity when discussing your own targets.
For a proposed agent, include permission checks, tool failures, duplicate attempts and changed source records in the evaluation. Our AI agent evaluation guide describes how those checks fit together.
Who owns the data, integration and operation work?
- Who provides access to source systems, and who confirms the meaning and quality of the data?
- Who implements and maintains each integration?
- Which changes require human approval, and where is that rule enforced?
- Who can inspect traces and usage, and what sensitive information do they contain?
- Who handles incidents, model or API changes, and failed background work?
- What documentation, access and operational knowledge are handed over?
Agree these responsibilities before comparing implementation estimates. A prototype demonstration and an operated application have different delivery obligations.
What does the next engagement include?
Ask for the decision the engagement supports, required inputs, outputs, exclusions and acceptance criteria. Separate assessment, implementation and ongoing operation in the commercial discussion. Where cost or effort depends on access and integration findings, record those assumptions.
AWS funding depends on the applicable programme, eligibility and approval. It should not be treated as secured before that process is complete.
CloudHero works with teams in Romania, the United Kingdom and Ireland. Our AI readiness assessment and implementation planning provides a starting point for defining the work. Explore our AI development and integration services and AWS credentials to assess the fit.