Our Methodology

Delivery and Collaboration

Our primary focus is to deliver outcomes aligned with your goals, not to staff engagements with headcount. We provide an integrated team with a single point of contact accountable to achieving the goals we align on.

• Single point of contact. A seasoned QA manager owns the engagement relationship, including coordinating the team, tracking performance against deliverables, and serving as your one point of contact.
• Cultural and user focus. We immerse ourselves both in your product and technical environment as well as how your organization works and who your users actually are. We operate as a shared, tightly integrated team rather than an external vendor working in a silo, and we stay grounded in how testing supports your organization’s strategy and the experience of the users you support.
• Tools and environment integration. Teams work inside the systems you already run, including test case management, defect tracking, CI/CD pipeline, and internal environments. We integrate into your workflow rather than asking you to adapt to ours. Where gaps exist, we can recommend enhancements or bring our own tools.
• Scalable engagement. You can easily scale support up or down as your needs change. Engagements are built around the deliverables defined in our agreed scope and we guarantee performance against those deliverables.
• Continuity. Teams are dedicated to build institutional knowledge over time, and we cross-train teams where possible to protect you against coverage gaps from vacation or attrition.
• US delivery. All teams are based in the United States.

Communication Philosophy

Our communication philosophy is based on the following tenets:

• Transparent. We are always clear about what work is being done, how, and why.
• Honest. We provide feedback on how things actually are, with no sugar-coating.
• Accountable. If we make mistakes, we own them and correct them.
• Responsive. We respond quickly and operate with a sense of urgency.
• Proactive. We anticipate and mitigate risk, working collaboratively to keep product quality as strong as it can be.
• Collaborative. We always collaborate closely to align on the best path forward.

Meetings and Reports

We align meetings and reports to your needs, including structure, details, and cadence.

We typically provide the following reports:

• Daily Report: Progress report on the work performed that day and plans for the next day.
• Monthly Budget Report: Monthly report covering service utilization and cost.
• Quarterly Business Review (QBR): Quarterly executive meeting focused on strategic alignment and governance, and ensuring there’s high return on investment.

We also establish escalation channels so blockers can quickly be resolved.

Traceability

Whenever possible, every test and defect ties back to the story that created it, so you can always answer what was tested, how, by whom, and when.

• Story intake. A ticket comes in describing a feature or function. We evaluate the acceptance criteria for gaps before testing begins, closing ambiguities with the business analyst or product owner.
• Test authoring. We write tests to exercise each acceptance criterion individually, with each test referenced back to the specific criterion that covers it whenever practical. Coverage is traceable at the criterion level, not just the story level.
• Execution. When development deploys code to an environment, we execute the corresponding tests and record where testing was conducted, version information, and any environment-specific configuration in use.
• Defect linkage. Defects are linked back to both the originating feature request and the specific failing test case. Each report clearly documents the issue and how it was discovered.
• Retest and resolution. After a fix is deployed, we retest. If the issue persists, the defect is updated accordingly. If a new issue surfaces during retesting, it’s logged as a new defect and enters its own cycle rather than being folded into the original. If the original issue is resolved, we annotate the defect with the configuration used, evidence of the passing result, and mark both the test and defect verified.
• Regression testing. Our teams support full pre-deployment testing in lower-level environments like staging.
• Production verification. Where necessary or feasible, the relevant tests (not a full regression) are re-executed in production after release.

Our standard process is to ensure every execution and defect is tied back to its originating story, providing the full history of what was tested, how, by whom, and when, so it’s ready both at the time of release and months later for any auditing. We tailor our approach to your specific environment, goals, and traceability requirements.

Security

QualityLogic has handled sensitive client data and IP for 40+ years, working with government agencies and regulated companies in healthcare, banking, insurance, and more.

• Compliance and certification. QualityLogic is SOC 2 Type II compliant, and our practices align with NIST SP 800-30 Rev.1 and NIST SP 800-53 Rev.5.
• Personnel. Our teams are US-based and undergo background checks, drug testing, and eligibility verification.
• No local storage or processing. Our default approach is to operate within your systems rather than store or process your data on QualityLogic infrastructure. Data stays where you already have it and we work in place rather than pulling it into our own environment.
• Data handling. Our default approach is to work in test environments without access to production data or other sensitive data, minimizing exposure regardless of engagement scope.
• Data residency. All data remains within the United States.
• Regulated data experience. We’re experienced in working with HIPAA, PCI, and similar data types. We regularly sign client Business Associate Agreements (BAA) or Data Processing Agreements (DPA).
• Testing and monitoring. We undergo regular third-party penetration testing and vulnerability scanning to promptly identify and remediate issues.
• Client-controlled environments. We often work from client-provisioned machines and connect through client VPNs, keeping work inside infrastructure you already control.
• Physical and network security. Our test labs are secure, badge-access facilities protected by firewall and domain authentication systems, intrusion alarms, and camera systems, with redundant network and internet connections and network-based backup.
• Dedicated oversight. Security is managed day-to-day by a dedicated security team, including onsite lab managers at each facility and companywide IT security management.
• Client-specific requirements. Where your security requirements exceed our standard approach, we document and plan for them as part of our engagement setup.

AI-Accelerated SDLC Assurance

AI coding tools make development faster. QA built for human-written code doesn’t scale to that speed on its own, and AI-generated tests share the same blind spots as the code they’re supposed to check. We establish quality systems and professional teams to make that speed sustainable instead of trading it for quality.

Tooling Alignment

We work with clients across a range of AI toolchains and ecosystems. We first align with you on your preferred tooling and any of your IT or security requirements. Where gaps exist, we can make recommendations or provide our own tooling. This is part of what sets the foundation for an engagement in an AI-accelerated SDLC.

Upstream and Downstream Quality

Quality can be managed upstream and downstream from the point of code generation. Upstream quality determines what gets built before code is generated, by improving the specification and intent the AI works from. Downstream quality catches what was built incorrectly after the fact. We believe a strong quality practice invests upstream first, as it’s cheaper to correct an unstated requirement than to find and remediate its consequences in a finished feature. While this has been true since before AI acceleration, it remains more critical today as AI accelerates and amplifies the outcomes of effective and ineffective quality practices alike.

This matters more with AI-generated code specifically because the “how” becomes opaque. A human engineer with years on a product can make intuitive leaps grounded in accumulated context. An AI agent, working within a bounded context window and generating output from statistical patterns rather than intuition, cannot replicate that judgment. When ambiguous specifications produce ambiguous or undesirable code, prompting the model to fix it doesn’t reliably correct the underlying misunderstanding. It can just as easily produce a quirky, hard-to-tune patch that resolves the symptom without addressing the intent gap. Since we lose visibility into how the code was produced, the most effective strategy is to ensure adherence to what was specified. That’s why it’s critical to push quality upstream to the specification itself rather than relying solely on downstream inspection of output.

Many techniques exist for maximizing code quality and intent alignment prior to generation, with the primary being spec-driven development (SDD), the creation of formal specifications as SDLC artifacts that drive and bound the generation of code. We work closely with teams to mature their SDD process and review specifications upfront for incorrect assumptions, vague or missing requirements, and a lack of testable criteria.

Ongoing Spec Review

• Shift Left. We shift review left, engaging at story approval before any code is generated.
• Iteration. We refine specifications iteratively, both through upfront review and as generated code and test results surface ambiguities or quality gaps. These are fed back into future spec review.
• AI evals. We help teams implement AI evals or LLM-as-Judge tooling to give teams an automated check on whether generated code matches stated intent. We pair this with human review of that output and regular optimization.

Specifications are living artifacts. We leverage the above techniques to create a feedback loop where each cycle of review improves the precision and quality of specifications, leading to an improvement in AI code generation quality.

Parallel Automated Coverage

The common method of work was to built test automation following feature development, and to grow that coverage over time. We build automation in parallel with development wherever possible, so functional coverage is in place when the feature is ready to ship.

• Selector seeding. We work with teams to seed unique identifiers, roles, or labels into requirements upfront, ensuring specs contain definite, testable selectors that give both development and QA the same reference points to build against.
• Parallel building. We built automation from those requirements in parallel with feature development.
• Convergence. Both development and QA converge at the same time. If they’re successful, it indicates the implementation has matched the intent. If there’s a mismatch, it indicates misalignment with the original requirements and we investigate and resolve this with development prior to release.

The process above enables development to occur rapidly, without growing verification debt from a QA process lagging behind. It also improves maintainability, since the UI is well structured with definite, stable references, and improves accessibility, since those same references support assistive technology.

Downstream Verification

AI can be effective in producing functional applications, but it falls short in areas of judgment. We focus human critical thinking and creativity in these areas precisely.

• User Acceptance Testing (UAT). We engage humans downstream for UAT and exploratory testing, not to re-verify functionality, but to evaluate intent, usability, and intuitiveness.
• Evaluating blandification. AI tends to sand off the edges of what makes a product unique because it defaults to statistically average solutions. We evaluate whether that’s happening, and whether it’s eroding what differentiates the product.
• Compatibility and accessibility. We focus human efforts on areas AI currently handles poorly, including accessibility and cross-environment compatibility testing.

The combination of techniques above ensures AI is leveraged to accelerate the SDLC, but human judgment and effort is applied where it is truly impactful.

Adversarial Testing

We utilize a different AI model to build tests than the one used to develop the code. Matching models typically confirm each other’s behavior and share the same blind spots, whereas different models surface unique failure modes. This is paired with human review and evaluation.

Test Data Management

Data is the major gate before test automation can run. Can it be staged, and how? Good QA outcomes depend on good, known data. We work with teams to establish best practices for setting up the data needed to test applications, including the establishment of QA-specific APIs, extending product APIs to support data seeding, direct data seeding in databases (including via stored procedures), and resetting databases through CI/CD. We also help establish test accounts with known-good data that can be used in isolation, so manual QA and automation don’t collide.

AI-Driven Development Guardrails

AI is most successful when scope is tightly limited and the context window is small. We know that isn’t always possible in modern development cycles, where engineers are increasingly building specifications more than they are building code. We focus on the following recommended guardrails for AI-driven development:

• Specs are evaluated before development begins
• Test automation is in place alongside the feature
• Unit tests are developed for individual units of functionality
• Features are tested as atomically as possible
• Test cases are generated from the evaluated and refined acceptance criteria in each feature, ensuring everything is documented to deliver the best possible experience once the build is complete

Between strong documentation and a rigorous QA process, we can maintain architectural stability across the SDLC and the product.

Test Automation and Engineering

How We Decide What to Automate

Automation isn’t right for every situation. Instead, we help you make an evidence-based decision by evaluating the following before any engagement:

• Time and cost of automated testing vs. manual, for the specific case
• Code-level readiness: modularity, presence of stable unique identifiers
• Product stability and any planned refactors that would undercut the ROI
• What ongoing maintenance will actually require
• Practical constraints on automation itself: workflows that reach into third-party systems, exceptionally long multi-role test paths, or any case where outcomes can’t be made predictable through data staging tend to resist automation

The output is a data-driven recommendation on which parts of the product suit automation and what risks could affect the build. Automation is only as reliable as the data behind it: without stable data setup and predictable outcomes, a test can’t function regardless of how well the rest of the case is built.

Build Approach

• Framework and language. We build in the stack your development team already uses, so you can own and maintain the automation code long-term if you wish. We’ll advise on framework or language selection based on your stack and QA goals, but the default is to match your team, not ours.
• Open-source tooling. Automation suites run on open-source, widely available tools. No proprietary licensing costs get passed on to you.
• Test case prioritization. Happy-path / positive cases get automated first, so the suite works as an effective smoke test and confirms critical functionality fast. Negative and lower-priority workflows are added after to make the suite robust.
• Atomic test design. Tests are built as atomically as possible, so a failure in one doesn’t cascade into false failures elsewhere. This keeps result fidelity high and testing speed up.
• Modular, reusable code with Page Object Model. Automation relies on stable, unique identifiers or consistent role / label / text selectors instead of brittle locator strategies (XPath or CSS selectors that break on every UI change). We use Page Object Model for maintainability and long-term stability.
• API and Frontend coverage. Where possible, we automate both layers for better granularity in reporting and faster root-cause identification. Where that’s not feasible, we’ll work with you to define the right approach.

Environment, CI/CD, and Maintenance

• We advise on automation infrastructure, including virtual infrastructure options if you don’t want to maintain it internally.
• For clients running continuous testing, we typically embed our team directly with developers, building automated test cases in parallel with feature development so automation ships with the code through CI/CD.
• We help establish pipelines with controlled gates, including dedicated test suites for PR-level approval, QA environment promotion, build-level suites, and full end-to-end regression for major releases or nightly runs.
• Reporting gets configured to your needs, from CI/CD build-system-only visibility to full email or HTML reports for stakeholders.
• Depending on your objectives, we can staff a team to build the suite and hand it off, or run a dedicated engineering team for ongoing automation and maintenance.
• You own the assets. All test automation scripts and artifacts we create are yours, delivered to your repository as they’re built or at program completion.

Digital Accessibility Testing and Assessment

Our accessibility audit methodology extends the industry-standard WCAG Evaluation Methodology (WCAG-EM) and W3C’s Accessibility Conformance Testing (ACT) methodology. It combines automated scanning with manual testing by trained auditors, including native assistive technology users. Neither one alone gets you there.

Automated Scanning

Covers only a portion of WCAG success criteria: the ones that can be identified programmatically. This flags errors, contrast issues, and items needing human judgment, like ARIA usage, accessibility features, and structural elements.

Manual Testing and Assistive Technology

Accessibility is fundamentally about usability rather than a simple checklist. Guidelines like WCAG establish a floor, but they’re open to interpretation and can’t fully capture whether an experience actually works for someone relying on AT. Manual testing closes that gap by having a human tester assess real behavior, flow, and cohesion in a way automated checks and guideline conformance alone cannot.

Manual testing follows, using assistive technology against unique page or screen templates: views representative of the functionality reused elsewhere in the product, which keeps testing efficient without sacrificing coverage. Testing spans:

• Desktop: JAWS, NVDA, VoiceOver for macOS
• Mobile: VoiceOver for iOS, Android TalkBack
• The latest three major browser and mobile OS versions, across a range of device models and resolutions

Testing across this range catches issues that only surface on certain combinations, and separates product defects from third-party issues in the assistive technology or browser itself, so remediation effort goes where it actually matters.

Defect Reporting and Prioritization

Defects get categorized using a three-tier model:

• Blocker. Noncompliant, and it stops the user from completing the workflow.
• Critical. Noncompliant, but the user can still complete the workflow.
• Enhancement. Not a conformance violation, but it makes the assistive technology experience confusing or difficult.

We can adopt whatever priority or severity ranking your tracking system already uses (Jira or otherwise). In practice, the underlying categories stay close to this structure, since ACR reporting itself only supports a limited set of conformance levels (such as Supports, Partially Supports, Does Not Support) to map findings back to.

Each defect report ties back to the specific WCAG success criterion, includes reproduction steps and supporting media, and lands in your preferred tracking system.

Deliverables and Additional Scope

Deliverables typically include an Accessibility Conformance Report (VPAT-format ACR) documenting conformance at a point in time. For broader engagements, scope can extend to:

• A remediation roadmap prioritizing fixes by user impact
• A digital accessibility practices evaluation, looking at organizational process and maturity, not just point-in-time defects
• Code remediation
• Document accessibility testing and remediation
• Multimedia and captioning assessment and remediation
• Remediation validation testing
• Accessibility training for client teams

We work with you collaboratively to identify the gaps and where our services best support your goals and ability to implement a scalable, cost-effective and self-sufficient accessibility program.

Have More Questions?

This page is intentionally high-level. If your evaluation gets to specifics (pricing, staffing plans, security documentation, a scoping call), contact us.