Aspecta earned the AWS Generative AI Competency, validating its ability to deliver generative AI solutions on AWS through a formal validation process and technical audit. For our customers, this reduces partner selection risk because the competency requires demonstrated practice & delivery processes and production customer examples with measurable impact. It also covers key technical areas including RAG, agents and tool-calling, prompt engineering, and fine-tuning where appropriate, together with enterprise requirements for security, privacy, compliance, Responsible AI, operations, and monitoring.
AWS Generative AI Competency context
AWS Generative AI Competency is a specialization within the AWS Partner Program that identifies and independently validates partners capable of delivering production-ready generative AI solutions. It helps customers choose providers in a field with many prototypes but fewer validated production deployments. Earning the competency requires evidence of real projects, a repeatable delivery model, sound architecture, security and operational practices, and measurable business impact against consistent AWS criteria.
Aspecta’s motivation to build and formalize its AWS Generative AI practice came from growing demand for production generative AI solutions and increasing customer focus on data security, hallucination risk, compliance, and auditability. In this environment, simply stating that a company can deliver GenAI is not enough. AWS competencies use structured validation of technical practices and customer success so customers can identify partners with demonstrated capabilities more easily.
Problem, goals, and scope of the initiative
Generative AI solutions can be prototyped quickly, but delivering them repeatedly at production quality is considerably harder. Aspecta’s main challenge was to establish a delivery model for GenAI solutions on AWS that could be demonstrated during an audit while also reducing risk for both the customer and the provider.
The objective was therefore not simply to “earn a certificate,” but to strengthen the internal delivery practice so it could withstand due diligence: a clear methodology and governance model, security and ethical principles, and the ability to document decisions, architecture, operations, and project impact.
This involved three specific groups of challenges:
- Evidence: prepare and maintain an evidence pack that clearly demonstrates repeatable practice from onboarding through implementation to adoption and success measurement, including artifacts that can be verified during remote technical validation.
- Technical: consistently cover the GenAI capabilities required by each use case, especially prompting, agent workflows and tool-calling, RAG, and model adaptation where appropriate, including how these solutions are tested, monitored, and operated.
- Enterprise readiness: make security, privacy, compliance, and Responsible AI a standard part of solution design and delivery rather than an add-on, with clearly defined guardrails and risk management.
The initiative therefore covered not only formal preparation and submission of the application, including self-assessment and validation checklists, but also production customer examples with documented architecture, operations, and measurable impact.
A key part was remote technical validation using evidence presented through screen sharing, together with internal strengthening of the delivery practice: templates, processes, security guardrails, monitoring, testing, and regression validation of prompts and workflows so the approach would remain sustainable after the competency was earned.
Delivery, timeline, team, and governance
The technical validation took the form of an audit with the external auditor Information Security Systems International (ISSI), an independent US consulting and audit organization authorized by AWS to perform technical competency validation. The audit examined architecture, data flows, the security model, operations, and measurable results in detail using actual project artifacts.
The validation relied on concrete evidence and the ability to examine individual solution areas in depth.
The initiative progressed through three connected stages. During preparation, the team defined the scope, consolidated architecture and operational materials, standardized templates for security, Responsible AI, and quality evaluation, and prepared production examples with quantified impact. The validation stage followed, including self-assessment, preliminary AWS review, and technical validation with the auditor. The final stage covered evaluation and award of the competency.
The initiative also demonstrated that Aspecta has a complete team with clearly defined responsibilities for generative AI projects on AWS, covering strategic management and delivery methodology, solution architecture, models and data, security, operations, and evaluation of business benefit. This is not a theoretical organizational model but a set of available roles with practical experience from production deployments.
The ability to cover the full solution lifecycle with internal specialists was one of the key factors in demonstrating a repeatable and auditable delivery model for customers.
| Role | Primary responsibility | Typical outputs and evidence |
|---|---|---|
| Executive sponsor | priorities, budget, scope decisions, and communication | business case, internal communication rules, risk decisions |
| Program or delivery lead | repeatable methodology from onboarding to adoption | delivery playbook, adoption framework, success measurement |
| Solution architect (GenAI/AWS) | RAG and agent architecture, integrations, scaling | architecture diagrams, failure scenarios, integration details |
| ML or GenAI engineer | prompting, quality evaluation, regression testing | version-controlled prompts, test scenarios, evaluation metrics |
| Data or knowledge engineer | ingestion, chunking, embeddings, indexes, updates, and deletion | ingestion mechanisms, chunking strategy, handling of sensitive data |
| Security and compliance lead | IAM, encryption, audit logs, controls | security model, log retention, incident procedures |
| MLOps or SRE | monitoring, alerting, operating model, SLO | observability, runbooks, capacity planning |
| Business analyst or value owner | KPI definition and before/after impact measurement | KPI framework, ROI model, impact section of the case study |
A key factor was Aspecta’s ability to demonstrate these practices and outputs on actual projects. Every phase of the solution was supported by existing project artifacts, from contractual and planning documents through architecture designs and security settings to operational procedures and measurable impact evaluations.
These were therefore not materials created specifically for the audit, but a standard way of working that we can apply consistently to customer projects and that demonstrates repeatable, auditable delivery.
Technology, models, data, and operations
The competency validates the ability to design generative AI solutions on AWS as complete systems rather than isolated models. The architecture covers the full flow from the user request through authorization and security controls, retrieval of context from internal sources, answer generation, structuring, citations, and auditable records. In practice, this mainly includes RAG scenarios, agent-based orchestration of multi-step tasks, and controlled prompting with quality testing.
Platforms and working with models
Solutions that prioritize deployment speed, a managed security model, and straightforward operations typically use fully managed services, most often Amazon Bedrock. Where deeper control over model behavior or adaptation to domain data is required, the delivery approach supports training and operations through Amazon SageMaker. The choice always depends on the specific use case, accuracy requirements, cost, and the required degree of customization.
Security and Responsible AI
Security and content guardrails are applied consistently at the service level, for example through Amazon Bedrock Guardrails. This provides consistent safeguards across models, agents, and the knowledge base while supporting privacy, compliance, and auditability requirements. Access controls, logging, and clearly defined operational procedures complement the security model.
Data and knowledge base
Production solutions use controlled processing of data sources, access management, and protection of sensitive information. Content update mechanisms, metadata management, and answer quality evaluation help maintain accuracy and relevance over time. This model supports secure work with different types of business content, from internal documents to operating procedures.
Operations and quality measurement
Solutions are designed for long-term operation. They include measurement of quality, performance, and cost, monitoring of behavior over time, and defined procedures for incidents and changes. This allows solutions to be deployed quickly and then developed systematically without reducing quality.
Capabilities, measurable results, and customer benefit
Validated capabilities
The competency confirms that we deliver generative AI solutions on AWS as a standardized service with defined procedures, quality measurement, and security controls.
- controlled prompt management: version control, testing, regression scenarios
- end-to-end RAG: data preparation, context retrieval, answer quality evaluation
- agent scenarios and integrations with internal systems using auditable operations
- a decision framework for model adaptation based on benefit and cost
- security model: access management, sensitive data handling, audit logs
- Responsible AI: reducing the risk of hallucinations and transparent use of sources
Measuring the benefit
We evaluate production deployments through a consistent KPI framework:
- request processing time before and after deployment
- answer quality and accuracy against source material
- reduction in operational or reputational risk
- user adoption and actual use of the solution
- return on investment based on time saved versus total cost
- latency and operational stability
Even without publishing specific figures, this approach demonstrates that solution benefits are measured systematically using production data.
Operations and security
- continuous monitoring of quality, performance, and cost
- incident management and controlled deployment of changes
- capacity planning
- least-privilege access and access auditing
- consistent security guardrails across the solution, for example Amazon Bedrock Guardrails
Key lesson
What matters is the ability to demonstrate architecture, security, operations, and measurable impact on real projects. The competency therefore relies on repeatable processes and evidence that reduce risk when deploying additional solutions.
“Earning AWS Generative AI Competency confirms that we deliver generative AI solutions on AWS in production, securely, and through repeatable processes. This is not a claim, but the result of independent technical validation and real deployments with measurable benefit. For our customers, this means lower risk and a faster path from design to production.” Peter Stročka, Managing Director of Aspecta
Conclusion
Conclusion
Earning AWS Generative AI Competency is not a one-time milestone for us. It validates the way we design and deliver customer solutions over the long term. Independent technical validation confirmed that we can deploy generative AI on AWS in production, securely, and with measurable benefit. Just as importantly, the result is not only additional references but a standardized delivery model that shortens the path from the first use case to a scalable deployment.
For customers, this means lower risk, a clear way to measure ROI, and a partner able to cover the full solution lifecycle, from architecture and work with data and models to long-term operations and development. The competency is therefore a foundation for further production projects in which generative AI becomes a controlled and auditable part of business processes.
