For most teams, QA begins after the build.

At HighLevel, it begins before a single line of code exists.

We’re redefining what it means to “test” by weaving AI directly into every stage of the SDET lifecycle — from design reviews and test generation to validation, automation, and performance analysis.

This isn’t about experimenting with AI; it’s about engineering confidence at scale. Our approach allows quality to evolve continuously with development — faster, smarter, and closer to the user experience than ever before.

This is not a story about tools or technology. It’s a story about how we’re redefining confidence in what we build.

🎨 Quality Begins with Design

Testing at HighLevel starts long before a feature is coded.
As soon as new design assets arrive, our QA process engages with them directly.

Design reviews once relied purely on manual observation—screens were checked for spacing, alignment, or brand consistency by eye. Now, our AI review systems examine the finer details automatically: interaction states, font hierarchies, accessibility contrast, and spacing across breakpoints.

Instead of waiting for inconsistencies to appear downstream, these insights are shared immediately with the design team, closing the gap between design and development.

This early validation ensures design integrity is preserved across every product surface, reducing rework and keeping the user experience consistent.

Quality doesn’t start at the test case anymore; it starts at the canvas.

AI doesn’t just see pixels — it sees intent.

📄 From Product Requirements to Structured Test Design(AI Driven)

Once PRDs and design flows are finalized, AI takes the lead in generating the first layer of test coverage.

Using a blend of NLP and vision models, it reads through PRD text, Figma flows, and UI components to automatically draft structured test cases. The system maps out core user journeys, identifies field-level validations, and even proposes boundary and negative scenarios — areas that usually demand hours of manual effort.

SDET’s then refine these AI-generated drafts, closing the gap between product intent and test coverage.

The result: faster, more consistent test design and better alignment between product, design, and QA from day one.

AI reads the PRD so testers can focus on the product.

🧠 Smart Execution with MCP (Model Comparison & Perception)

During execution, our MCP (Model-Centric Platform) ensures visual precision at scale.

It compares live application screens against design baselines, detecting even subtle deviations in alignment, spacing, color tone, and visual hierarchy. Whether it’s a two-pixel drift or a missing shadow, MCP flags it instantly.

This has been key in catching design regressions early and maintaining visual integrity across platforms — ensuring every release looks and feels exactly as intended.

Because even a two-pixel drift can break perfection.

🤖 AI Review Before Peer Review

Before any human review, every test case passes through our AI reviewer — a pre-validation layer designed to ensure structural and logical soundness.
This process ensures logical completeness, coverage across both edge and negative scenarios, and eliminates redundancy in structure.

This automation clears structural noise so peer reviews can focus on higher-order discussions — quality strategy, impact, and risk — instead of format and completeness.

Our goal isn’t simply to write tests faster—it’s to write tests that age well.

AI clears the clutter so humans can focus on quality, not structure.

⚙️ Accelerating Automation with Cursor

Automation always remains at the heart of the SDET process.
Engineers simply describe what they want to test — a Playwright flow, an API validation, or a regression check — and Cursor generates framework-compliant scaffolds automatically.

This drastically reduces setup time, enforces consistent coding patterns across projects, and frees our engineers to focus on logic and optimization instead of boilerplate setup.

Your next Playwright script starts with a sentence, not a setup.

🧩 LLM as a Judge for AI-driven Scenarios

As we integrate AI-powered features across our platform, we’ve faced a new kind of testing challenge: evaluating intelligence itself.

When testing features powered by Large Language Models, we let the models themselves act as judges.

Instead of relying on rigid, rule-based assertions, the LLM evaluates responses for contextual accuracy, semantic relevance, tone, and intent alignment.

This approach helps us validate generative or conversational outputs with nuance and precision — ensuring the experience feels coherent, intelligent, and on-brand.

We test intelligence using intelligence.

📈 Performance That Learns

Traditional load and performance testing rely on static thresholds.
But as our systems grow more dynamic, those thresholds often fail to reflect real-world patterns.

Our performance testing framework now incorporates adaptive learning, analyzing results after every run and refining its expectations based on historical data. Over time, this turns performance testing into a continuous learning process—where every test improves the accuracy of the next.

The system gets smarter as the product matures, evolving in parallel with our infrastructure.

Where every load test makes the next one smarter.

Because real performance isn’t fixed — it’s adaptive.

Engineering Confidence, Not Just Tests

AI hasn’t replaced our SDETs — it’s amplified them.
By automating the repetitive and mechanical, we’ve made room for deeper investigation, creativity, and empathy.

Every layer of our process, from design validation to performance analysis, exists to bring quality upstream and to ensure every feature reflects the same level of care our users expect from HighLevel.

What used to be a linear checklist is now a living ecosystem of feedback and refinement—one where quality doesn’t wait for the end of development but grows with it.

Reimagining quality means making it continuous, intelligent, and profoundly human.

And we’re just getting started.