Common questions about QA services, AI testing, and release support.

Release risk is Well Tested's decision layer for the question 'should we ship?'. It combines engineering activity, Postgres-first table diff outcomes, and SEO/public-site QA signals so teams can decide whether to approve, investigate, or block a release. See how it fits in the broader release readiness picture at /blog/what-is-release-risk.

Table diff compares two datasets and highlights schema changes, row-count differences, aggregate shifts, and keyed mismatches. In practice that means you can see whether a release changed the shape of a table, dropped records, or introduced suspicious drift before it reaches production. Try it at /demo or read the full comparison at /blog/table-diff-vs-schema-diff.

Schema diff tells you the table definition changed — column added, type altered, constraint removed. Table diff tells you whether the data story changed too: row counts shifted, aggregates moved, keyed records disappeared. Schema diff is necessary; table diff is sufficient for release confidence. Teams that rely on schema diff alone often miss real data risk. Full breakdown at /blog/table-diff-vs-schema-diff.

No. Green CI proves the pipeline ran and your encoded tests passed. It does not answer whether the release is safe to ship. Green CI tells you detection worked; release readiness tells you whether you should approve. Well Tested combines engineering signals (CI activity), data signals (table diff), and public-site signals (SEO QA) into one release decision flow. See the full separation at /blog/release-readiness-beyond-green-ci.

The shipped warehouse connector is Postgres-first today, so the live product and demo focus on Postgres table diff and expectation-style checks. Other warehouses may be added later, but we do not market them as shipped until they exist in the product.

SEO QA checks public-site trust signals such as sitemap coverage, robots rules, canonical tags, Open Graph quality, structured data, and important route presence. It is intended to catch marketing-site regressions before a deploy or launch goes live. The full checklist is at /blog/seo-qa-before-deployment.

Manual QA catches what a human tester looks for — UI bugs, UX friction, unclear flows. It does not systematically catch data drift, schema changes, or public-site regressions across every deploy. Well Tested automates the checks that manual QA would either miss or spend too much time重复ing: table diffs, SEO signal regressions, cross-environment data consistency. The services side (founder-led QA) handles the exploratory and judgment-heavy work; the product side handles the systematic release signals that should run on every ship.

Unit tests verify code logic — does this function return the right output for a given input? They do not verify whether the data in your production or staging database is consistent with what the release will produce. Table diff catches data-level regressions that no unit test can see. Well Tested complements CI by adding the data-observation layer that sits between 'tests passed' and 'ship it'.

AI testing involves validating AI models and applications to ensure they function correctly, produce accurate results, and meet performance standards. This includes testing model accuracy, bias detection, integration testing, and ensuring AI-powered features work as intended.

LLM (Large Language Model) testing includes validating prompt responses, testing for hallucinations, ensuring context management works correctly, optimizing token usage, and verifying that LLM integrations produce reliable and accurate outputs for your application.

Yes. Well Tested is designed to fit into existing CI/CD workflows. The table diff, expectation checks, and SEO QA runs can be triggered from your pipeline, providing a structured pass/fail signal alongside your existing unit and integration tests. The /demo route shows the table-diff workflow; the /demo/table-diff workspace is the authenticated setup path.

We can typically begin testing within 1–2 business days after project kickoff. For urgent projects, we can often start the same day. The exact timeline depends on project scope and current availability. Use the /contact page to describe your release timeline.

Absolutely. We do not believe in one-size-fits-all packages. Every project is unique, and we work with you to create a customized QA solution that fits your specific needs, budget, and timeline. Start a conversation at /contact.

We work with Seed through Series B teams across SaaS, FinTech, HealthTech, EdTech, e-commerce, and AI-powered product companies. Our focus is on teams that ship frequently and need systematic release confidence rather than one-off QA gates.

Our services include manual testing, automated testing, AI/LLM testing, performance testing, API testing, QA consulting, test documentation, and usability reviews — customized per project. The specific mix is agreed at kickoff based on your product stage and release risk profile. See the full scope at /services.

We use industry-standard testing methodologies, comprehensive test coverage, automated testing where appropriate, and thorough documentation. Our founder-led approach ensures personal attention to detail and quality in every project. The product side (table diff, SEO QA, release risk) enforces systematic checks that run on every release, independent of manual bandwidth.