Ship with confidence—before users feel the breakage.
Well Tested combines founder-led QA services with product surfaces for release risk, Postgres table diff, and SEO checks—so your team spends effort where evidence matters, not on busywork.
Release risk & readiness
Engineering events, SEO QA, and table-diff snapshots roll into one release-risk view—before you merge or tag.
Manual + automation depth
Blend exploratory QA, scripted checks, and CI-aware coverage in one engagement model.
Postgres-first data checks
Schema, row-count, aggregate, and keyed table diffs against Postgres today—additional warehouses are on the roadmap, not in the box yet.
SEO & public-site QA
Route and metadata checks so launches don’t ship broken sitemaps, OG tags, or schema signals.
Signals
Release · Data · SEO
Coverage
Manual · Auto · Product
Outcome
Ship-ready clarity
Next steps
Start with the interactive demo, then compare packages or dive into services.
Product workspace (preview) — release risk, QA signals, and a clear ship decision
Interactive preview
Preview how intelligent signals, targeted QA, and release intelligence come together in one workspace.
Well Tested
Release & QA workspace
Scope the risk
Review what changed and decide where QA effort matters most.
Run targeted QA
Test the journeys that could hurt trust, conversion, or launch quality.
Deliver next steps
Return a concise risk summary, fix priorities, and what to cover next.
Current review scope
Live output
Review what changed and decide where QA effort matters most.
Release changes, product hotspots, and AI or SEO surfaces are narrowed into a practical review scope.
Risk map prepared
Changed flows and sensitive release areas are identified before testing starts.
Priority paths selected
The team knows which user journeys deserve immediate QA attention.
See table diff and release signals in one flow
Open the interactive demo—compare tables, frame the decision, and understand impact before you buy services.
Customizable solutions
Shape QA around how you ship: intelligent scoping for AI-assisted and traditional testing—launch readiness, regression, automation, and monitoring without a one-size-fits-all motion.
Mix and match these building blocks—same structure, different emphasis each engagement.
- Discovery audit before a launch
- Manual QA for critical user journeys
- Automation planning and CI setup
- AI and LLM release validation
- Package expansion based on findings
- Ongoing monitoring and regression support
Tailored scopes
Start with flows, integrations, or AI surfaces that carry the most risk—then expand coverage as priorities shift.
Flexible engagement
One-time audit, fixed package, or ongoing support—matched to your stage instead of a rigid playbook.
Founder-led delivery
The person shaping the plan stays in the loop—no handoffs through anonymous layers.
Manual plus automation
Pick the mix that fits real workflows: exploratory QA, scripted checks, and CI-aware coverage.
Product workspace (engagement view)
Engagement example
A high-level walkthrough of how Well Tested scopes risk, runs QA, and returns an action plan.
Well Tested
QA review workspace
Scope the risk
Review what changed and decide where QA effort matters most.
Run targeted QA
Test the journeys that could hurt trust, conversion, or launch quality.
Deliver next steps
Return a concise risk summary, fix priorities, and what to cover next.
Current review scope
Goal
Catch the issues most likely to damage launches, conversion, or AI behavior before customers see them.
Live output
Review what changed and decide where QA effort matters most.
Release changes, product hotspots, and AI or SEO surfaces are narrowed into a practical review scope.
Risk map prepared
Changed flows and sensitive release areas are identified before testing starts.
Priority paths selected
The team knows which user journeys deserve immediate QA attention.
The same workflow flexes for launches, regressions, or AI behavior.
One-time review or ongoing support—the pattern stays consistent: surface risk, run targeted QA, ship clear next steps.
Release-risk snapshot
Engineering signals roll up into a score you can defend in a pre-release review.
Targeted QA & SEO checks
Scope critical journeys and catch metadata or routing issues before customers do.
Data diff & expectations-style checks
Compare Postgres source vs target tables with clear deltas—not spreadsheet guesswork.
Right now in the walkthrough
Scope the risk
Release changes, product hotspots, and AI or SEO surfaces are narrowed into a practical review scope.
How it works.
A straightforward QA process designed for modern software teams that need release confidence without adding heavy process.
Comprehensive QA Services for Modern Teams
QA consulting, manual testing, automated testing, API testing, AI testing, and LLM testing for software teams that need clearer release confidence.
Comprehensive functional, usability, and exploratory testing to ensure your application works reliably.
E2E, API, unit, and integration testing with CI support for continuous quality assurance.
Specialized testing for AI models, AI features, and integrations to improve accuracy and reliability.
Comprehensive LLM testing covering prompt quality, responses, hallucinations, and context behavior.
Load, stress, and scalability testing to understand how your application behaves under pressure.
REST, GraphQL, and SOAP API testing to improve backend reliability and contract confidence.
Strategic QA planning, workflow review, and tool guidance to improve your testing approach.
Usability reviews, UX debugging, and accessibility testing to improve real user experience.
Pick a package. Adjust the scope.
Start with a fixed scope around release quality, automation, AI validation, performance testing, or ongoing monitoring, then adjust from there.
The perfect starter for teams who need a senior QA partner to define a testing approach, build coverage, and run critical functional checks.
Best for
You're shipping fast, but bugs are slipping through and QA is done ad hoc (if at all).
- Risk-based QA strategy document
- Manual functional tests for core flows
- Exploratory testing on releases
Automate your test suite, catch regressions before your users do, and speed up releases.
Best for
Manual QA doesn't scale. Your team wastes time running the same tests every release.
- E2E tests with Playwright/Cypress
- API tests (e.g., Postman, REST-assured)
- CI/CD pipeline setup
Testing tools don't know how to validate AI models — but I do. Let's verify your ML/LLM systems are accurate, reliable, and safe.
Best for
AI systems are unpredictable. LLMs hallucinate. Models degrade. Standard QA doesn't catch it.
- Prompt and response validation for chatbots
- Model testing: accuracy, edge cases, fairness
- Red-teaming simulations
Ensure your app doesn't crumble under load. Identify bottlenecks before your users do.
Best for
Your backend and frontend may work in dev — but can they handle real users?
- Load and stress test plans
- Backend performance profiling
- Frontend render analysis (Lighthouse, Web Vitals)
QA doesn't stop at release. I offer ongoing test maintenance, alerts, and quality monitoring.
Best for
Bugs creep back in. Tests get stale. No one notices until users complain.
- Regular test suite maintenance
- Automated test monitoring
- Release regression reports
Clear pricing direction without another full package grid.
Use the landing page to understand fit. Use the pricing page when you need the package-by-package ranges, notes, and tradeoffs.
Planning note
Packages start at fixed entry points, then flex around real QA needs.
Most teams do not need every layer of testing on day one. The pricing page is where the full ranges live. The discovery call is where scope gets shaped.
Quick expectation
Start with a package or audit, then expand only where the product risk justifies it.
Know what deserves testing before the next ship.
One working session focused on how you release today—cadence, stack, and where failures actually hurt. You get a practical read on manual coverage, automation, and AI validation—not a bloated audit deck or vague “best practices.”
What we cover
Walk your real release path—not a generic checklist—and spot where quality breaks today
Name the user journeys where a bug costs revenue, trust, or velocity
Leave with prioritized next steps for manual, automated, and AI-backed checks
Open to an early case study?
If the engagement is a strong fit and the work is worth showcasing, we can talk about a case study later—optional, and only with your approval.
Example focus areas
What the first QA conversation usually clarifies.
Session
Founder-led
Focus
High-risk flows
Output
Action brief
Find pressure points
Exploratory QA
Pressure-test high-risk flows before they reach customers.
CI quality gates
Decide what should run every push and what stays manual.
API confidence
Catch contract drift and unhappy-path failures earlier.
Exploratory QA
Pressure-test high-risk flows before they reach customers.
CI quality gates
Decide what should run every push and what stays manual.
API confidence
Catch contract drift and unhappy-path failures earlier.
Decide next moves
AI and LLM checks
Review prompts, outputs, and regression risk in product context.
Exploratory QA
Pressure-test high-risk flows before they reach customers.
CI quality gates
Decide what should run every push and what stays manual.
AI and LLM checks
Review prompts, outputs, and regression risk in product context.
Exploratory QA
Pressure-test high-risk flows before they reach customers.
CI quality gates
Decide what should run every push and what stays manual.
What you leave with
A sharper QA direction across manual review, automation priorities, and AI validation risk. No filler. No bloated audit deck.
Priority tracks
Manual, automation, AI
Follow-through
Concrete next-step brief