THE PROBLEM

AI-powered testing is accurate and slow. 

If you've run mobile tests using an AI-first approach, you already know the trade-off: you get less flakiness, you ditch brittle selectors, and your tests survive UI changes. But every step of every test goes through an LLM inference call,  and that adds up fast.

A single test with 10 steps takes roughly 1.5 minutes end-to-end when AI is evaluating each screen. Scale that to a full regression suite and you're looking at hours, not minutes. 

The deeper issue isn't just speed. It's that AI is probabilistic. Run the same test twice and you might get slightly different behavior. That's fine for exploration. It's a problem for a CI pipeline that needs to be deterministic.

"AI is great at figuring things out the first time. But running it again for the same screen is just a waste." — Asad Abrar, Founder & CEO, Drizz

Most testing tools have responded to this problem in one of two ways: accept the slowness and market accuracy as the selling point, or skip AI entirely and revert to code-based selectors with self-healing patches. Both are compromises. Drizz chose a third path.

THE FEATURE

Intelligent Visual Caching: AI when you need it, deterministic speed when you don't.

Drizz's Intelligent Visual Caching is a hybrid execution model that combines the accuracy of AI with the predictability and speed of screen-level caching. Here's exactly how it works:

How It Works

• First run: Every step is evaluated by AI. Drizz understands the screen, identifies the right element, executes the action, and, critically takes a visual snapshot of that screen state.
• Subsequent runs: Before calling the AI, Drizz compares the current screen against the cached snapshot. If they match, it executes directly from cache. No LLM call. No latency.
• When things change: If the screen has changed, say,  a new element, updated layout, different content, Drizz detects the mismatch and routes back to AI. The cache gets updated. The test stays accurate.

What Makes It Smart: Dynamic Cache Matching

This isn't a naive pixel-diff. Drizz's cache logic understands the difference between content and structure. If you have a search field that sometimes shows "San Francisco" and other times shows "New York", the layout is the same, only the text value has changed. The cache still works.

This means caching survives the kinds of real-world variability that break simpler approaches: dynamic text, localization strings, user-specific data, A/B variants with identical structure. You get cache hits even when the screen isn't pixel-perfect identical.

BY THE NUMBERS

The performance impact is not marginal,  it's dramatic.

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And the cache gets smarter the longer you use it. On longer test flows,  think 80, 100, or 120+ steps,  teams are seeing cache hit rates climb to roughly 90% of steps as screens stabilize across reruns. That means on a 100-step test, only ~10 steps actually need fresh AI interpretation. The rest resolve from cache in half the time.

The gains compound at scale. Real customers running larger suites have seen even more dramatic improvements, since the cache hit rate increases as tests mature and screens stabilize.

INDUSTRY CONTEXT

Where Drizz fits in the mobile testing landscape.

The mobile testing industry has been wrestling with the AI speed problem since LLM-based execution became viable. Here's how the major players have approached it, and where Drizz differs:

Applitools Applitools is the visual testing leader for web. Their Visual AI engine handles comparison, not execution. They don't run natural-language test steps, they validate screenshots against baselines. Applitools and Drizz solve adjacent problems. Applitools checks what the screen looks like; Drizz decides what to do on it.

Perfecto / BrowserStack These platforms provide device infrastructure and have added AI-assisted authoring. Their core execution engine remains selector or script based. Speed is not a bottleneck for them because they're not running LLM inference per step. The trade-off is higher maintenance when UIs change.

Katalon Katalon's visual testing uses layout and content-based comparison modes. It is conceptually similar to how Drizz's cache differentiates between structural and content changes. Their application is to visual regression testing; Drizz uses the same logic to decide whether to invoke AI during functional test execution.

GPT Driver / Mobileboost GPT Driver runs all LLM calls at temperature 0.0 (deterministic outputs) and pins model snapshots to prevent silent upgrades. They offer step-level caching for previously successful screen-prompt pairs. This is the closest to what Drizz does, but their caching is primarily prompt-hash based,  if the prompt changes slightly, the cache misses. Drizz's visual-first matching is more resilient to prompt variation and content drift.

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The industry consensus is clear: pure AI execution is too slow for CI, and pure code-based testing is too fragile. The winning approach is a hybrid: AI on first contact, determinism on reruns. Drizz built that hybrid from the ground up for mobile.

WHY IT MATTERS

Two problems solved, not one.

The conversation around AI testing usually focuses on accuracy vs. speed as a binary choice. Intelligent Visual Caching breaks that frame by addressing both dimensions simultaneously.

1. Speed: CI-grade execution on reruns The first run is slower and that's the cost of AI reasoning. Every subsequent run, assuming the screen hasn't changed, is cache-only. No network call. No inference latency. Just a screenshot comparison and direct execution. This is what makes Drizz viable in a PR pipeline, not just a nightly regression run.

2. Determinism: Consistent results across runs AI is probabilistic by nature. The same prompt on the same screen can occasionally yield different element identification. Cache is binary: either the screen matches or it doesn't. On reruns, Drizz's behavior is entirely deterministic. The test either uses cache (same screen → same result) or calls AI (changed screen → fresh reasoning). No gray zone.

3. Accuracy: AI where it counts When a screen changes:  a new element, a redesigned flow, a feature flag rollout, the cache misses by design. Drizz routes back to AI, which handles the change correctly. The cache doesn't become stale silently. It stays live and accurate because the cache-miss condition is the actual change in the app.

GETTING STARTED

Visual Caching is live , no setup required.

Intelligent Visual Caching is available to all Drizz users today. There's nothing to configure. On first run, Drizz builds the cache automatically. On subsequent runs, it uses it. If you want to force a cache refresh, say, after a major redesign you can invalidate from the Drizz dashboard.

If you'd like to see it running on your actual app, book a demo and we'll walk you through your first cached run.

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