Human QA with auto-corrections for AI-generated code
Live QA assistants test features, PRs, and interfaces, and reports are prepared in a format that can be immediately fed to CI/LLM agents for auto-fixing.
Cases and examples
Manual review of AI-generated code before merging
Checked a pull request with auto-generated code: found 7 UI regressions, documented reproduction steps and hints for auto-fix, closed all in a single CI run.
Exploratory + smoke testing for AI bots
We've compiled scripts for Playwright/Puppeteer and 15 manual scenarios: we catch instabilities, immediately attach screenshots and log diffs so the LLM agent can rewrite the steps.
UX assessment of onboarding with noise-resistant reports
Crowdtesting generated a lot of "noise," but we filtered out duplicates and turned that noise into a checklist for auto-tuning texts, forms, and validations.
👥 Who is it for
🎯 Use Cases
🛠️ Technologies Used
What is required of you
- Access to the test environment and repository with the launch instructions.
- List of critical user flows and measurable goals (errors, conversion, speed).
- Expected report format: JIRA/GitHub Issues, markdown packages, JSON for agents.
- Contacts of the person responsible for accepting fixes.
What you get
- Report with reproduction steps, screenshots/logs, and priorities.
- Format for auto-correction: structured Markdown/JSON that can be fed to an LLM agent or pipeline.
- Smoke/regression checklists + ready-made playbooks for CI.
- Summary of false positives and noise filtering rules from crowdtesting.
- The suggested fixes and hints for prompts to make the agent fix code hands-free.
📋 Order a service
Fill out a short brief — I will respond within 24 hours.
📝 Enter your details
🎁 What you will get
Report with reproduction steps, screenshots/logs, and priorities.
Format for auto-correction: structured Markdown/JSON that can be fed to an LLM agent or pipeline.
Smoke/regression checklists + ready-made playbooks for CI.
Summary of false positives and noise filtering rules from crowdtesting.
The suggested fixes and hints for prompts to make the agent fix code hands-free.
💬 After submitting the application, I will contact you using the provided details to clarify the specifics.