Appvance, the technology leader in generative AI for software quality assurance (SQA), announced the GENI Transformation Factory, a groundbreaking expansion of its AI platform that can now generate, transform, and regenerate every QA artifact across the software development lifecycle.

With the addition of API data generation from OpenAPI specs, Appvance IQ now provides the industry’s only unified, AI-first testing platform that autonomously generates scripts and data across UI, API, and end-to-end flows.

Built with Model Context Protocol (MCP) support, the GENI Transformation Factory allows enterprises to use any compliant large language model (LLM) — open source, commercial, or homegrown. With this release, Appvance extends its decade-long leadership in AI-first testing, introducing a new level of flexibility and completeness for enterprises pursuing AI-led digital transformation.

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An AI Factory for QA Artifacts

QA has long relied on a chain of artifacts — business requirements, epics, user stories, Gherkin scenarios, test case summaries, step-by-step test cases, and executable test scripts. Historically, each artifact was created and maintained separately, often inconsistently, leading to gaps in coverage, costly rewrites, and fragile connections between requirements and tests.

The GENI Transformation Factory changes that. Any artifact can now serve as input to generate any of the others, instantly. For example, test cases can be transformed back into user stories if requirements documentation has fallen behind. Business requirements can flow downstream into scripts, while existing scripts can regenerate higher-level summaries or even business requirements. This two-way upstream/downstream capability ensures consistency across the QA chain, no matter where the process begins.

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In early trials the GENI Transformation Factory reduced human labor by 85% to 93% compared to creating or updating the artifacts from scratch.

“AI can now design, generate, and regenerate the full set of QA artifacts with limited human intervention,” said Kevin Surace, Appvance CEO. “With the GENI Transformation Factory, QA finally has a living, bidirectional system of record. Teams can move downstream from requirements to execution or upstream from scripts to business rules in minutes. And because it’s built on MCP, enterprises can choose whichever LLM best aligns with their strategy. This is the future of quality assurance: an AI factory for every QA artifact.”

How It Works

The GENI Transformation Factory builds on Appvance’s patented Digital Twin technology and its proven AI Script Generation (AISG) and GENI™ engines. Together, they enable both downstream and upstream creation of QA artifacts:

• Requirements to Execution – Input business requirements to generate epics, stories, scenarios, cases, and scripts.
• Execution to Requirements – Input test cases or scripts to regenerate upstream artifacts, such as summaries, user stories, or business requirements.
• Seamless Consistency – Ensure every QA artifact is synchronized across the chain, eliminating costly rewrites and misalignments.

MCP ensures enterprises can execute these transformations with any compliant LLM, deployed on-premises, in private cloud, or through commercial providers.

Why MCP Matters

Enterprises have historically been locked into a single LLM vendor when adopting AI-first testing. MCP, described as a “universal translator” for AI, enables Appvance clients to swap models in and out with no disruption. This future-proofs QA infrastructure, giving CIOs and CTOs control over which models to deploy based on security, governance, or strategic priorities.

Key Benefits

• CIOs and CTOs – Drive AI-first digital transformation while maintaining freedom of LLM choice and enterprise compliance.
• QA and Development Teams – Eliminate manual creation and upkeep of artifacts, freeing teams to focus on strategy and innovation.
• Enterprises – Release faster with higher quality by ensuring all QA artifacts stay aligned, no matter where the process begins.
• Service Providers – Deliver client-ready QA automation that adapts to any artifact state, with dramatically reduced labor costs.