Fixed-scope packages for release quality, automation, AI validation, and monitoring.
Choose a package as the starting point for stronger release confidence, then adjust scope around your product, team workflow, and actual shipping risk.
What these packages are and when to use them
Each package is a fixed-scope engagement built around a specific release-confidence outcome. Packages work well as starting points — you can expand scope, combine packages, or move to a retainer as the product and release cadence matures.
Base QA is the right starting point if you need systematic testing coverage without an existing QA process. Automation applies if you have a product that ships frequently and need regression checks between releases. AI/ML Validation is for products with model-powered features that need systematic evaluation beyond functional testing.
All packages are scoped for Seed–Series B teams. For early-stage products, start with the FAQ to understand what kind of release confidence applies at your stage, or book a discovery call to map your specific stack and release flow.
Problem
You're shipping fast, but bugs are slipping through and QA is done ad hoc (if at all).
Solution
I'll become your embedded QA partner — building a lightweight, strategic quality plan, and executing targeted functional testing across frontend, backend, and APIs.
Benefit
Get peace of mind and confidence in your product's reliability — without hiring a full-time QA team.
Deliverables
- Risk-based QA strategy document
- Manual functional tests for core flows
- Exploratory testing on releases
- Integration with your workflow (Slack/Linear/Notion)
Problem
Manual QA doesn't scale. Your team wastes time running the same tests every release.
Solution
I build automated tests for your critical flows and wire them into your CI/CD pipeline (e.g., GitHub Actions, GitLab, Jenkins).
Benefit
Ship confidently. Every push runs tests. You focus on building — I'll make sure nothing breaks.
Deliverables
- E2E tests with Playwright/Cypress
- API tests (e.g., Postman, REST-assured)
- CI/CD pipeline setup
- Test coverage reports
Problem
AI systems are unpredictable. LLMs hallucinate. Models degrade. Standard QA doesn't catch it.
Solution
I test supervised models for accuracy, drift, and edge cases — and validate LLM/chatbots for prompt coverage, hallucination, and bias.
Benefit
Ship responsible, production-ready AI features that perform under pressure.
Deliverables
- Prompt and response validation for chatbots
- Model testing: accuracy, edge cases, fairness
- Red-teaming simulations
- Ongoing model regression monitoring
Problem
Your backend and frontend may work in dev — but can they handle real users?
Solution
I simulate traffic, identify bottlenecks, and give you actionable fixes.
Benefit
Deliver smooth, fast, scalable experiences.
Deliverables
- Load and stress test plans
- Backend performance profiling
- Frontend render analysis (Lighthouse, Web Vitals)
- Database query performance review
Problem
Bugs creep back in. Tests get stale. No one notices until users complain.
Solution
I maintain your test suite, monitor releases, and alert you when something breaks — before your customers do.
Benefit
Peace of mind, every sprint. Your QA is always up-to-date.
Deliverables
- Regular test suite maintenance
- Automated test monitoring
- Release regression reports
- Custom dashboards or alerting (optional)
Scope and recommendations depend on your product, release cadence, and current coverage.