Insight Library
Buying and rollout questions, answered in product language.
This page collects the editorial layer behind the landing experience. Each article is written for release owners, QA leads, and product teams deciding how GenRafi, AccessRafi, and RunRafi should fit into one operating model.
Why scenario generation is replacing static test design in modern release teams
Rafi Gen changes how teams start automation: instead of drafting brittle flows from scratch, release owners define intent in plain language and shape executable scenarios around the business path that matters now.
Why teams start here
Use this brief to understand how intent-driven generation reduces first-draft test design, shortens time to first run, and creates a cleaner path into execution and accessibility review.
Manual rewrites
Doc-grounded scenario sets
Execution-ready drafts
You will get
Accessibility can no longer be a late-stage audit
Rafi Accessibility Engine works best when accessibility checks sit inside the release path itself, so teams catch semantic, focus, and screen-reader issues before launch pressure turns them into backlog debt.
WCAG 2.2 AA coverage
Low false positives
Journey-level review
No-code automation works when execution is resilient, not when it is visual only
Rafi Run proves that no-code only scales when locator recovery, execution stability, and controlled reruns are treated as core platform behavior instead of a cosmetic recording layer.
Recorder-only tools
RafiRun self-healing
Multi-platform reuse
Cloud speed vs enterprise governance is no longer a tradeoff
The strongest QA platforms now let teams start fast in cloud, then move into stricter rollout patterns without replacing the authoring, validation, and reporting model they already adopted.
Initial rollout
Control depth
Workflow continuity
How product teams map onboarding, checkout, and regression into one reusable release flow
The most efficient teams stop treating onboarding, purchase, and regression as separate automation projects. They standardize one reusable operating pattern and let AI tailor the scenario surface to each release moment.
Separate suites
Shared flow model
RafiRun orchestration fit
How we use LLMs across generation, accessibility review, and execution support
LLMs are most useful when they are placed inside a controlled QA workflow. The goal is not generic chat output, but better scenario shaping, clearer review context, and more reliable operator decisions.
Scenario shaping
Accessibility context
Operator support