Sogeti AI-Powered Quality Engineering and Testing Services

Capgemini Group

GenAI-powered Quality Engineering boosts test speed, clarity, and precision—cutting costs and time-to-market with smart validation, automation, and Cognitive QA.

GenAI Amplifier: AI-Powered Quality Engineering on Azure

Problem Statement

Software delivery cycles are shrinking, yet system complexity continues to grow. The real bottleneck isn’t writing code—it’s taking accountability for what we ship. True accountability requires clarity—clear requirements, intent, and expectations. Too often, the transition from idea to implementation is where quality erodes, causing defects, technical debt, and reduced agility. Traditional QA approaches simply cannot keep pace.

How can teams leverage Azure and AI to accelerate software delivery without compromising quality?

Why Is This Important?

Without early, reliable feedback grounded in clear requirements and structured test design, teams move quickly but blindly. Poor quality creates costly defects, downtime, and loss of customer trust.

Why Now?

Generative AI (GenAI) has matured to support critical Software Quality Engineering tasks such as clarifying requirements, creating robust test strategies, and ensuring validation coverage. Integrated with Azure’s ecosystem (Azure OpenAI, Azure DevOps, Azure Web Apps, Azure MySQL, Azure Blob Storage), GenAI now transforms quality engineering from mere test execution to strategic quality enhancement.

Our Solution

GenAI Amplifier accelerates software quality engineering directly within Azure-based workflows. Designed by expert consultants for practical use, Amplifier helps teams rapidly structure, refine, and validate requirements, user stories, and test assets with speed, consistency, and security.

Built upon Sogeti’s proven TMAP methodology, Amplifier seamlessly integrates with your Azure environment, enhancing existing investments. Leveraging Azure OpenAI, Amplifier delivers rapid test creation, automated ambiguity detection, and requirement refinement to significantly enhance your Azure software delivery lifecycle (SDLC).

Key Capabilities

  • Expert-Guided Workflows: Azure-based, RAG-enhanced processes streamline test design.
  • Ambiguity Detection: Improve clarity, reducing defects early.
  • Risk-Based Test Generation: Fully traceable, AI-driven test creation.
  • Cognitive QA Dashboard: Real-time descriptive, prescriptive, and predictive insights via Azure analytics.
  • Secure Azure Environment: Robust compliance features with built-in data security.

Differentiators

  • Full Azure Integration: Direct use of Azure OpenAI, Azure DevOps, and Azure Container Apps for comprehensive test automation.
  • Trusted AI Framework: Ethical AI usage aligned with Azure’s responsibility principles.
  • Self-Healing Test Automation: Natural Language Processing reduces rework as Azure-hosted applications evolve.

How Amplifier Extends Your Azure Usage

  • Accelerated Azure Deployments: Streamlined validation pipelines decrease Azure application setup times.
  • Enhanced Azure Investments: Utilize your existing Azure environment, maximizing resource efficiency and productivity.
  • Optimized Azure Resources: Automated testing and validation ensure efficient utilization of Azure’s infrastructure.

Business Value

  • Faster Time-to-Market: Speed up Azure-hosted application delivery.
  • Scalable and Secure: Deploy in your Azure tenant, maintaining full compliance control.
  • Lower TCO: Decrease rework, manual effort, and infrastructure overhead through optimized Azure utilization.
  • Azure-Aligned: Fully complies with Azure best practices and security standards.

Azure Marketplace Offerings

GenAI Amplifier Pilot (2-month, client-hosted on Azure): €25,000, providing comprehensive setup and feature access for evaluation within your Azure subscription.

Accelerate your Azure quality engineering journey. Book a free 2-hour session with our experts.

🔗 Explore Amplifier on Azure today!

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https://store-images.s-microsoft.com/image/apps.9113.871aa8f5-d194-49ee-9a7d-59ab38488f49.934c0cb2-4421-46b2-b2d2-57b9b6297664.434359d5-532d-4b30-8368-fb8927e71989