Series A is the inflection point where informal QA breaks. Before the raise, you had five or eight engineers, a product that was still taking shape, and a reasonable excuse to move fast and fix things as they came up. After the raise, the team doubles. The shipping cadence increases. New features are being built across more areas of the product simultaneously. And the informal "we test it before we ship it" approach that worked at six people stops working at fifteen.
The question is not whether to build a QA process. The question is what to build in what order, so that the process actually fits the team and the product rather than being a generic framework bolted onto a startup that has not slowed down enough to use it.
Month 1: Establish the Foundation
The first month is not about automation. It is about getting honest about the current state and establishing the minimum structure that makes the rest of the process possible.
Define done. The most important thing you can do in month 1 is define what "ready to release" actually means for your team. Not a vague sense that "it works," but a written, agreed-upon definition: which test cases must pass, what defect severity threshold blocks a release, who has sign-off authority. This definition becomes the foundation of every release decision going forward.
Set up defect tracking with severity levels. If you are using Jira or Linear, establish a clear severity taxonomy. P0 means production is down or data is corrupted. P1 means a critical user path is broken. P2 means functionality is impaired but workaround exists. P3 means minor issue, low impact. Without clear severity definitions, every bug becomes equally urgent and nothing gets prioritized effectively.
Document the 10 most critical user paths. Have someone walk through the product and write down the 10 flows that matter most to your users or to your revenue. These become the starting scope for your regression suite. Not every page. Not every feature. The 10 flows that, if broken, would cause immediate customer impact.
Run your first manual regression. Execute the 10 critical paths manually and document what you find. This is your baseline. Everything going forward will be measured against it.
Nothing in month 1 should be automated. The foundation comes first.
Month 2: Coverage and Process
With the foundation in place, month 2 is about integrating QA into the sprint workflow and building the test case library that the automation will eventually run.
Write test cases for every story during sprint planning. Before development starts on a story, QA writes the test cases based on the acceptance criteria. This has two benefits: it finds ambiguities in the requirements before implementation (much cheaper than finding them after), and it gives developers a clear, testable definition of done that they can verify as they work.
Run regression before each release. Use the manual regression suite established in month 1 as a release gate. If the 10 critical paths pass, the release is cleared. If they don't, the release is blocked until the regression failure is resolved.
Identify the three highest-risk areas for automation priority. Look at your defect history from the past three months. Which areas of the product have generated the most bugs? Which areas have the highest customer impact when something breaks? Those are your automation candidates. Typically this is the payment or billing flow, the core product loop, and the authentication system.
Choose your automation framework. For most Series A startups building on modern web tech, Playwright is the right choice. It is actively maintained, has excellent TypeScript support, runs cross-browser natively, and integrates cleanly into GitHub Actions. If you have an existing Cypress or Selenium suite, evaluate whether to extend it or migrate rather than building a second suite in parallel.
Month 3: Automation and CI Integration
Month 3 is when the investment in months 1 and 2 pays off. With defined processes, documented test cases, and a clear automation priority list, you can build automation that will actually be maintained and used.
Build automation for the three highest-risk paths. Write Playwright tests for the three areas identified in month 2. Focus on both the happy path and the most common failure modes for each. A payment flow automation that only covers successful transactions is half-done.
Integrate into CI/CD so tests run on every PR. Once the Playwright suite is running reliably, integrate it into your CI pipeline so that every pull request triggers the automated regression. This is the single highest-leverage change you can make to your QA process. Developers get immediate feedback on regressions in their own PRs, while they are still in context on the change that caused them.
Establish test result reporting. Set up reporting so that test results are visible in your PR review process. Failed tests should block merging. Flaky tests should be flagged for investigation rather than silently retried. The reporting creates the accountability structure that keeps the automation suite healthy over time.
Monthly retrospective on escaped defects. At the end of month 3, review how many production bugs made it through your new QA process and which ones. Each escaped defect is a data point about where the process has a gap. This retrospective drives the coverage priorities for month 4 and beyond.
The mistake most Series A teams make in month 2: trying to automate everything at once. Automate the payment flow and the core product loop first. Everything else can wait.
What to Automate First (and What to Leave Manual)
Automation is not the goal. Reliable, fast feedback is the goal. Automation is one mechanism for achieving it, but it is not appropriate for every type of testing.
Automate: Regression for the core product loop. Payment and billing flows. Authentication flows. API contract tests. Any test that you need to run on every PR or every deployment. These are repetitive, well-defined, and have a clear expected outcome that a script can verify.
Leave manual: Exploratory testing of new features. Usability evaluation. Any test that requires human judgment about whether the behavior feels right, not just whether it matches a predefined expected output. New feature validation before the feature is stable enough for automation to be written against it.
The most common mistake at the Series A stage is automating low-risk, stable areas of the product (like the settings page or the profile edit flow) while leaving the high-risk payment and auth flows on manual regression. Automate by risk, not by convenience.
The QA Maturity Target for Series A
QA maturity models describe how structured and reliable a team's quality process is. Most early-stage teams are at Level 1 (reactive: no defined process, testing happens ad hoc) or Level 2 (repeatable: some defined process, but inconsistently followed).
The target for a Series A company by month 6 is Level 3 (managed): a defined QA process that is consistently followed, automated regression running in CI/CD, documented coverage targets, and a measurable defect escape rate tracked over time. Level 3 is the point at which quality stops being reactive and starts being predictable.
Level 3 does not require a large QA team. It requires a clear process, the right tooling, and consistent execution. One experienced QA engineer embedded in a well-structured process delivers more than a team of three working without one.
Building a QA process at your Series A startup?
We have set up QA processes for companies at exactly this stage. A 30-minute call will show you what a 90-day build looks like for your specific team and stack.
Book a Free CallCommon Pitfalls at the Series A Stage
Skipping the foundation and jumping straight to automation. Automation without a defined process produces fast, consistent execution of the wrong tests. The foundation work in month 1 is not optional overhead. It is what makes the automation valuable.
Letting test coverage lag behind new features. Series A is defined by rapid feature development. Every new feature that ships without corresponding test cases is coverage debt that compounds. If the QA function cannot keep pace with the feature development rate, escalate that as a resource constraint, not a QA problem.
No ownership of QA. "Everybody owns quality" is a sentence that means nobody owns it. Quality needs a named owner who is accountable for the process, the metrics, and the coverage. That owner might be a dedicated QA engineer, a consultant, or an engineering lead with explicit QA responsibility. But the ownership must be explicit.
Treating QA as a pre-release checklist instead of a continuous process. The pre-release checklist is the last line of defense, not the primary one. The primary defense is QA embedded throughout the sprint: test cases written during planning, testing running alongside development, defects filed while there is still time to fix them. If your QA process only activates at release time, you are doing damage control, not quality assurance.