Understanding the GenAI Competency on AWS

When it comes to adopting generative AI, the stakes are high. Success depends not just on the technology itself, but on having the right expertise to guide implementation, ensure security, and scale responsibly. With so many providers offering GenAI services, it can be difficult to know which ones truly have the depth of experience to deliver on these goals.

That’s where the AWS Generative AI Competency comes in.

Much more than a badge, this competency represents a rigorous, third-party validation of a partner’s ability to build and operationalize GenAI solutions using AWS technologies. It signals that a partner has not only mastered the technical requirements but also demonstrated real-world success, adherence to ethical AI practices, and a commitment to ongoing support.

In this blog, we’ll break down what the AWS GenAI Competency means, how it can help you evaluate potential partners, and what to look for as you navigate the GenAI landscape. We’ll also share a practical example of what successful GenAI adoption looks like.

Why the GenAI competency matters

The AWS GenAI Competency represents validation rather than marketing. It assures customers that partners have demonstrated the capacity to deliver GenAI systems that are technically sound, operationally robust, legally compliant, and ethically responsible. Organizations exploring or deploying generative AI gain confidence that their AWS GenAI Competency partner has received independent validation on these critical dimensions.

For AWS partners like Caylent, maintaining this competency requires ongoing capability building, methodological refinement, and real-world delivery. Success demands technical depth, business breadth, and unwavering focus on responsible innovation.

As the generative AI landscape evolves with large multimodal models, new regulatory frameworks, and shifting customer expectations, the value of rigorous, end-to-end competency standards continues to increase.

Key requirements for achieving the AWS GenAI competency

Customer strategy development

AWS partners seeking the GenAI competency must demonstrate their ability to evaluate organizational readiness at the start of any generative AI engagement. This evaluation encompasses business objectives, data landscape, technical maturity, and cultural context. Partners guide clients through discovery workshops, readiness assessments, and strategy formulation to ensure solutions align with objectives and identify realistic use cases.

A retail company case illustrates this requirement. When data exists in departmental silos that hinder effective AI deployment, competent partners recommend cross-team data sharing and rapid prototyping practices. This approach establishes the foundation for sustainable GenAI innovation.

GenAI application development expertise

Partners must maintain verifiable expertise with underlying GenAI technologies, including Amazon Bedrock, Amazon SageMaker JumpStart, and supporting architectures. This proficiency requires certifications, practical training, and ongoing skills development. Technical capability encompasses designing, testing, and operationalizing GenAI solutions that seamlessly integrate with customers' existing workflows and systems.

Caylent’s work with Venminder, an Ncontracts company, showcases GenAI application development at scale. Looking to automate document processing and compliance assessments, Venminder partnered with Caylent to build a generative AI solution that reduced review times by over 5x and enabled Venminder to scale without increasing headcount. Data retrieval and answering compliance queries that previously took hours can now be accomplished in minutes. Venminder’s clients now benefit from quicker, more accurate compliance assessments as well as access to new insights, helping them meet critical regulatory deadlines.

Foundation model selection and customization

Selecting appropriate foundation models requires a methodical approach that considers cost, latency, context window, customization requirements, and regulatory needs. Partners benchmark models for specific use cases and evaluate options for prompt engineering, retrieval-augmented generation (RAG), parameter-efficient tuning (PEFT), or full fine-tuning based on business requirements.

Caylent’s collaboration with Pipes.ai demonstrates the importance of thoughtful foundation model selection and customization. To build a GenAI-powered Voice AI service with Agentic AI capabilities, Caylent integrated AWS AI services with best-in-class text-to-speech and speech-to-text technologies. The solution enables seamless, human-like conversations and advanced capabilities like open-ended questioning, real-time appointment rescheduling, and contextual intelligence. Pipes.ai now has a scalable and cost-effective system that delivers 24/7 support and measurable results, including up to 70% in potential cost savings, a 10–15% increase in qualified calls, and a 30–40% drop in opt-out rates.

Custom model lifecycle management

Building on foundational models often requires fine-tuning or specialty model creation. Competency partners demonstrate robust lifecycle management, including training, evaluation, deployment using services like Amazon SageMaker AI, serving models for inference, and monitoring real-world performance. They address cost and scalability concerns while maintaining documented, repeatable processes.

Caylent’s work with Symmons exemplifies custom model lifecycle management in action. To enhance their Evolution® smart water management platform, Caylent integrated ML and Generative AI models using AWS services to detect leaks, inefficiencies, and usage anomalies across commercial properties in real time. These models continuously retrain on live and historical data, improving detection accuracy and delivering tailored recommendations to facility managers. Automated deployment, real-time monitoring, and iterative updates ensure sustained model performance and scalability. This AI-powered approach has helped Symmons customers save over 80 million gallons of water in a single year, reduce response times, and prevent costly infrastructure damage, proving the long-term value of robust model lifecycle management.

Privacy, security, and compliance

Data privacy, security, and compliance assume critical importance in generative AI projects where sensitive data, privacy regulations, and model outputs carry far-reaching implications. Competency partners maintain comprehensive data inventories, employ anonymization and risk management mechanisms, and adhere to relevant regional and industry-specific regulations.

Caylent’s work with Z5 Inventory showcases how GenAI can improve efficiency while maintaining strict healthcare compliance. Using Amazon Transcribe and Amazon Bedrock, Caylent helped automate video transcription for hospital inventory counts, cutting processing time from nearly an hour to just minutes and improving data accuracy. By transitioning toward audio-only transcription, the solution also reduces HIPAA compliance risks, demonstrating how AI can streamline operations without compromising patient privacy.

Responsible and ethical AI

AWS requires GenAI Competency partners to operate with a documented commitment to ethical AI practices. This includes policies for bias detection and mitigation, transparency in model behavior and limitations, user safety measures, and ongoing evaluation of risks associated with generative outputs. Users receive tools to understand, control, and challenge AI-generated results when necessary.

Caylent’s collaboration with Trulioo highlights how responsible and ethical AI can enhance internal efficiency without compromising data integrity or user trust. To support Trulioo’s global identity verification platform, Caylent deployed a secure, Generative AI-powered chatbot using Anthropic Claude V2 and Amazon Kendra to deliver accurate, context-aware responses to engineers’ queries. Built on Amazon Bedrock, the solution protects sensitive data, ensures transparency through linked sources of truth, and enables ongoing oversight via prompt engineering and RAG techniques. By empowering Trulioo’s teams with trustworthy, explainable AI, the solution streamlines onboarding while upholding high ethical standards in AI deployment.

Ongoing maintenance and support

Generative AI solution delivery extends beyond initial implementation. Competency partners offer structured stabilization, maintenance, and support services to help customers manage operational risks, improve model performance, and resolve user issues. This includes clear escalation procedures, defined support response times, and proactive solution evolution based on customer feedback.

After deploying an AI-powered document search assistant, partners track feedback on the relevance of results and model errors. This information drives iterative retraining and enhancement to meet the client's evolving information retrieval needs.

Caylent's approach to the AWS GenAI competency

As an AWS Premier Tier Services Partner, Caylent demonstrates GenAI Competency through methodology spanning advisory and strategy, rapid prototyping, scaling to production, and operational excellence. Their co-delivery model works alongside customers to ensure that GenAI deployments address real business challenges and deliver measurable outcomes.

Caylent invests in regular team upskilling through certification programs, continuous training resources, and initiatives like an internal "Bounty Board" for AWS certifications. This organizational commitment ensures that both technical consultants and business stakeholders maintain current expertise as GenAI advances.

Early engagement stages feature ideation and strategy workshops that guide clients through use case discovery, data strategy assessments, and readiness evaluations. Caylent designs data modernization roadmaps, helps establish cross-functional AI teams, and coaches organizations through adopting iterative, experiment-driven GenAI practices.

Technical delivery utilizes frameworks for selecting and benchmarking foundation models across various dimensions, including cost, latency, and fluency. Customization practices are tailored to client needs, employing prompt engineering for rapid results or full fine-tuning where domain accuracy is critical. Model lifecycle management encompasses automated deployments, robust monitoring, and retraining, often implemented using infrastructure-as-code for efficient scaling.

Caylent emphasizes privacy, compliance, and AI ethics through documented data inventories, risk assessments, and anonymization protocols. They communicate transparently about model limitations and mitigation strategies for risks and biases. 

Conclusion

The AWS GenAI Competency provides clarity into what distinguishes leading partners in the generative AI space. From strategic advisory to technical excellence and responsible AI delivery, competency partners like Caylent drive the adoption of transformative GenAI solutions that create measurable business impact. Case studies such as BrainBox AI demonstrate that achieving and practicing AWS GenAI Competency translates into real-world benefits, enabling customers to innovate securely, efficiently, and ethically on the AWS platform.

AWS GenAI competency FAQ

What is AWS Generative AI competency?

The AWS Generative AI Competency is a validation program that identifies and recognizes AWS Partners who demonstrate technical proficiency and proven success in implementing generative AI solutions for customers. Partners who achieve this competency have been independently validated on critical dimensions, including customer strategy development, technical expertise with AWS GenAI services, foundation model selection, model lifecycle management, privacy and security, ethical AI practices, and ongoing support capabilities.

What is AWS GenAI?

AWS GenAI refers to Amazon Web Services' suite of generative artificial intelligence services and capabilities that enable organizations to build, deploy, and manage generative AI applications. This includes services like Amazon Bedrock (a fully managed service offering foundation models), Amazon SageMaker JumpStart (which provides pre-built models), and supporting infrastructure for developing custom generative AI solutions. These tools enable businesses to integrate text generation, image creation, content summarization, and other generative AI functionalities into their applications.

What are AWS competencies?

AWS Competencies are designations that recognize AWS Partners who have demonstrated technical expertise and proven customer success in specialized solution areas. These competencies help customers identify qualified partners for their specific needs. Partners must undergo a rigorous validation process involving technical reviews, customer references, and proof of successful implementations. AWS offers various competency programs across industries (like healthcare, financial services) and technical domains (like machine learning, security, and generative AI).

How is Amazon using generative AI?

Amazon is implementing generative AI across multiple aspects of its business and service offerings:

1. Through AWS, Amazon provides generative AI services, such as Amazon Bedrock, which allows businesses to access foundation models from companies like Anthropic, AI21 Labs, and others.
2. Amazon is integrating generative AI into its products and services, including using AI to enhance search and discovery on Amazon's retail platform, improve customer service experiences, optimize logistics and supply chain operations, and power content creation for Amazon's entertainment services.
3. Within its operations, Amazon leverages generative AI to streamline processes, improve warehouse efficiency through predictive analytics, and enhance decision-making across its extensive business ecosystem.
4. Amazon is also investing in the development and training of its own large language models and foundation models to power next-generation AI capabilities for its customers and internal use cases.