Requirements to Reviewed Test Cases
Upload or maintain requirement documents, generate candidate test cases, inspect AI quality controls, approve or reject outputs, and preserve human-edited changes.
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Product Documentation
Orlithic QA Console User Guide
A searchable guide for using Orlithic QA Console to manage requirements, generate AI-assisted test cases, review quality signals, run manual QA cycles, test APIs, track bugs, inspect diagnostics, and prepare release decisions.
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Overview
Orlithic QA Console is a QA workspace for controlled pilot teams that need a single place to manage requirements, generate and review AI-assisted test cases, run manual QA cycles, test APIs, track bugs, inspect diagnostics, and use contextual AI support.
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Workspace Scoped
Core data is stored through workspace-aware APIs backed by PostgreSQL.
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AI Assisted
Generate tests, inspect quality signals, and use AI analysis with human review gates.
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Governance Ready
Trace requirements, approve generated tests, link bugs, and inspect release readiness.
Quick Start
- Open the console and sign in with your pilot account.
- Select or create the workspace you want to operate in.
- Upload requirements in the Documents area or create structured requirements manually.
- Generate or import test cases, then review AI confidence, source refs, and quality flags.
- Approve suitable test cases before adding them to test runs.
- Create a test cycle and test run, execute cases, attach evidence, and create bugs from failures.
- Use Release Readiness to inspect coverage, defects, stale cases, API pass rate, and risk signals.
Core Workflows
Manual Execution and Defects
Create test cycles and runs, add selected test cases, execute statuses, save actual results and evidence, then create linked bugs from failed run items.
API Testing and Intelligence
Build collections, configure environments, run API requests through the backend queue, review assertions, inspect response intelligence, and link API requests to test cases or bugs.
Release Readiness
Use the dashboard to inspect requirement coverage, approved tests, cycle progress, blocked and failed results, open high-severity bugs, stale cases, security findings, and deterministic release-risk reasons.
Feature Guide
Documents
Store requirements, folders, document versions, original uploads, parsing jobs, and smart refresh previews for changed requirement sections.
AI Generator
Generate, import, review, deduplicate, approve, reject, organize, and execute test cases with source requirement context.
Traceability
Map requirements to tests, API requests, runs, bugs, source documents, and automation changes so gaps are visible before release.
Bugs and Evidence
Create linked defects, attach run evidence, track lifecycle state, and preserve context from failed test execution.
Diagnostics and Admin
Review operational events, worker health, audit logs, failed jobs, API queue recovery, limits, storage, and workspace members.
AI Chat and RAG
Use contextual AI assistance tied to workspace documents and app state, with index health and retry controls available for admins.
Admin, Storage, and Pilot Notes
Current workspace data is backed by PostgreSQL and workspace-scoped APIs. Authentication is implemented with Clerk-aware middleware and server-side user resolution, with RBAC enforced across workspace routes.
Low-risk UI preferences and draft-only values may still use browser storage. Treat the current product as a controlled pilot: core workflows are backend-backed, while advanced enterprise hardening continues incrementally.
Object storage is available for original documents, evidence files, bug attachments, and API binary references. A dedicated antivirus scanner workflow remains a future production-hardening item for untrusted uploads.
FAQ
What is Orlithic QA Console?
It is a QA workspace for requirements documents, AI-assisted test generation, structured imports, API testing, automation review, bug tracking, diagnostics, release readiness, and contextual AI chat.
Is the app backend-backed?
Yes. Core workspace data uses PostgreSQL and workspace-scoped APIs. A few low-risk UI preferences and draft-only values may still remain in browser storage.
Do AI-generated test cases require review?
Yes. AI-generated cases carry confidence scores, source requirement references, quality flags, review status, and human-edited tracking so reviewers can approve or reject them before execution.
Can I run API tests?
Yes. API collections, requests, environments, queued runs, response intelligence, assertions, linked bugs, and reports are supported through backend-backed API Testing flows.
What should admins monitor?
Admins should review usage limits, workspace members, audit logs, operational events, worker health, RAG index health, failed jobs, API queue recovery, and storage status.
Can I export this guide?
Yes. Use the export buttons at the top of the page to download this guide as HTML, Word-compatible DOC, or Markdown.
Support and Next Steps
For pilot teams, start with a small workspace, upload a representative requirement document, generate a limited test set, review AI quality signals, run a manual test cycle, and inspect release readiness before broad rollout.