AWS Partner AI Services Competency, Generative AI Consulting Services
AWS AI Services Competency · Generative AI Consulting Services

CloudHero, a DevOps and cloud engineering company, announced today that it has achieved the Amazon Web Services (AWS) AI Services Competency. The designation recognizes CloudHero’s demonstrated technical proficiency and proven customer success helping organizations build and improve AI applications on AWS.

For CTOs, product leaders and engineering teams, choosing an AI implementation provider means assessing more than model expertise. An application needs to work with existing software, handle data appropriately, produce measurable results and remain maintainable after launch.

CloudHero helps customers address those requirements through production AI engineering: application development, integrations, evaluation, deployment and operational visibility.

What does this mean for your AI project?

The AWS Competency Program helps customers identify AWS Partners with validated technical expertise and proven customer experience. For organizations evaluating CloudHero, this designation provides an additional qualification signal alongside the team’s customer references.

CloudHero’s approach helps you:

  • Build AI into your existing product. Connect models and agents to application data, APIs and workflows, with defined controls over the actions they can take.
  • Measure quality before expanding usage. Establish evaluation scenarios and compare changes against a shared baseline so your team can see where an AI system improves or regresses.
  • Understand operating costs and unexpected behavior. Trace interactions, errors and model usage to investigate problems and make informed architecture decisions.
  • Keep development moving after handover. Give your engineers documented components and clear extension points for future features.

Two customer projects show what this work looks like in practice.

Mindory: an AI architecture its developers could extend

AutiHD, the company behind Mindory, already had a live AI planner and an internal development team. As the product grew, its AI architecture made persistent memory, reliable task actions and new behaviors harder to implement.

CloudHero rebuilt the production AI layer using Amazon Bedrock and Amazon Bedrock AgentCore. One lead engineer and one solution architect delivered the work in 35 working days, integrating improvements with the existing product during development.

The architecture added persistent memory, controlled tool use and interaction tracing. Documentation and a developer guide gave AutiHD’s engineers defined places to extend the system.

Read how CloudHero rebuilt Mindory’s production AI architecture.

RaptorCI: evidence that AI code review was improving

RaptorCI’s AI code review engine already processed live pull requests. Its team needed a trusted way to measure detection quality and determine whether changes improved the product.

CloudHero and RaptorCI established a frozen evaluation set of 60 pull-request scenarios, then improved the engine running on Amazon Bedrock.

On that evaluation set:

  • Detection of marked real issues increased from 42% to 66%.
  • Precision, the share of reported findings that were correct, increased from 51% to 60%.

RaptorCI gained a repeatable way to compare configurations and identify regressions. These results describe the evaluation set; they do not represent detection accuracy across all production pull requests.

Read how CloudHero measured and improved RaptorCI’s AI code review.

Start with an AI readiness assessment

If you are planning an AI feature, developing an agent or preparing a pilot for production, start with the decisions that will shape its business value.

An AI readiness assessment with CloudHero can help you examine the intended outcome, available data, application integrations, evaluation criteria, security requirements and operating costs. Together, we can identify the gaps to address and scope a practical next step.

Request an AI readiness assessment