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AI Workflow Framework

Use AI to streamline your testing workflows, cut manual effort, and scale your QA like never before.

Quality assurance is getting more demanding – our AI workflow framework keeps it under control.

Designed around your challenges, ready to grow with you, and proven in real-world QA.

Testing often gets sidelined due to time constraints and scattered expertise. At the same time, pressure for quality and traceability is growing. Our AI workflow framework addresses this by automating where it counts, easing your team’s workload, and delivering consistent, predictable results – even in complex environments.

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We don’t offer standard solutions – we build the right one with you. Fully aligned with your systems, workflows, and governance. Modular, flexible, and designed to grow with your needs.

Our AI workflow framework integrates role-specific AI assistants and captures domain knowledge without creating silos between business and IT. With AI-driven insights from real usage data, you gain transparency and control over your testing process.

Our approach: Technologically leading, regulatorily safe, and operationally smart. Ideal for organizations that see quality as a strategic priority – not just a task.

Unified control and complete transparency

The AI solution that brings structure and visibility to your QA process.

icon-software_testing Analysis & Management With detailed analytics and continuous monitoring, our AI framework gives you full visibility and control over your test processes – securely and efficiently.
icon-training Knowledge Management & Data Integration Our AI workflow framework links central knowledge bases with real-time data using RAG-based answer generation and automated interpretation of ongoing testing – for a seamless bridge between information and execution.
icon_AI training Role-Based AI Assistants Our assistants are customized for specific roles, provide structured input support, and ensure consistency and traceability through clearly defined parameters.
versatility Orchestration and Workflows Dynamic workflows, parallel prompt handling, and structured results let you automate complex testing tasks with speed, consistency, and precision.
icon-qa_methodology AI Integration Easily connect the AI workflow framework to your current systems, work with different LLMs, and manage resources efficiently to get the most out of your AI investment.
Less Effort. More Clarity. Reliable Results.

Launch your AI testing journey with a AI workflow framework tailored to your needs.

From strategic analysis to hands-on rollout – we make sure your entry into AI fits your organization: customized, scalable, and secure.

FAQ

Common questions about our
AI Workflow Framework

How do I get started with the AI workflow framework in my test environment?

We begin with a real use case from your organization. Together, we define clear goals, relevant data sources, and technical requirements – so your AI workflow framework fits seamlessly and delivers value from day one.

Which systems can the AI workflow framework connect to?

Our AI workflow framework connects effortlessly with Jira, Confluence, GitHub, and your CI/CD pipelines. Its modular design allows for both cloud and on-prem setups – aligned with your IT infrastructure and regulatory needs.

Does the AI workflow framework meet regulatory requirements?

Our AI workflow framework is developed for use in regulated environments. It ensures compliance with data protection standards, enables traceable execution, and includes full audit trails and clear process documentation.

How quickly can the AI workflow framework be deployed?

Typically, the AI workflow framework can go live within a few weeks – depending on how it integrates with your systems and infrastructure.

What makes your AI workflow framework different?

Our AI workflow framework isn’t off-the-shelf. It’s custom-built, deeply integrated into your workflows, and provides role-based support that turns complex testing into manageable, trackable steps – delivering real impact and reducing daily workload.