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Web, mobile, and APIs
Customer-facing and internal web apps, mobile web and native apps, the APIs behind them, and the integrations between them, with automation architected to scale across a whole portfolio, not just one app.
Journey run
Sample run- Sign-up to first purchasePassed
- Chrome, Firefox, Safari, and mobilePassed
- API calls behind each step38 checked
- Order written to the databaseVerified
- Slowest endpoint1.8 s
One slow endpoint on watch.
What we cover
- End-to-end testing of critical user journeys, from sign-up to checkout to account management
- API and integration testing, including traffic through gateways like Apigee
- API regression generated from the real calls behind each screen
- Cross-browser and real-device coverage, including mobile
- Automation built into GitHub Actions, Jenkins, or GitLab CI, sharded for speed
- Modernizing Selenium, Cypress, and home-grown suites
- Mobile web and native app testing, on real devices
- Load, stress, and soak testing with k6, built from your existing journeys
- Native iOS and Android automation with Appium, AI-assisted through the official Appium MCP server
Browsers, devices, and mobile apps
One suite, every surface your customers use. Playwright covers Chromium, Firefox, and WebKit, and the same tests run on real browsers and devices through your BrowserStack or Sauce Labs account, so you see what someone on an older Safari or a mid-range Android actually gets.
Mobile web is covered by the same journeys, on real mobile browsers rather than a resized desktop window. For native iOS and Android apps, we build Appium suites that live in the same pipeline and report alongside the web results, so one release view covers the whole product.
Mobile Automation Starter
Three weeks, fixed price, for teams shipping native iOS and Android apps with manual regression or a fragile suite. You end with your most important journeys running on real devices on every build.
What we build
3 weeks · iOS and Android · fixed price
- Week 1Framework set up: one TypeScript codebase on WebdriverIO and Appium, shared page objects for both platforms, and your first three journeys running on simulators and emulators.
- Week 2Up to ten critical journeys automated, such as sign-up, login with MFA, payments, and push-notification deep links. AI-assisted authoring through the official Appium MCP server, reviewed by us line by line.
- Week 3Real-device runs on your BrowserStack or Sauce Labs account, wired into your CI, with guarded self-healing and a handover session.
What we need from you
Kept small on purpose
- Test builds: an .apk and an .ipa, or TestFlight and internal track access
- Test accounts, including one with MFA if your app uses it
- A device cloud account, or we recommend the device list and you create it
- A developer for a few hours to add accessibility IDs where screens are missing them. We send the exact list.
How AI fits into mobile, concretely
Locators, ranked before they’re used
| Strategy | Decision |
|---|---|
| Accessibility ID | Preferred same ID on iOS and Android |
| Android resource-id, iOS predicate string | Accepted platform-specific |
| iOS class chain, UiSelector text | Flagged breaks with copy changes |
| XPath | Rejected slow and brittle on mobile |
The Appium MCP server ranks candidate locators for each element. Our commit and CI guardrails enforce the ranking, so a rejected strategy never reaches the suite.
One healing engine, web and mobile
When a screen changes, a heal is accepted only if it matches exactly one element, and assertions are never healed. Every heal is proposed as a reviewable change, not applied silently.
Judged like the rest
AI-drafted mobile tests pass the same LLM-as-judge quality gate as web tests before they’re merged.
One page object, both platforms
// login.screen.ts: shared by the iOS and Android suites export class LoginScreen { // Accessibility IDs first. No XPath. get email() { return $('~login-email'); } get password() { return $('~login-password'); } get submit() { return $('~login-submit'); } async signIn(user: TestUser) { await this.email.setValue(user.email); await this.password.setValue(user.password); await this.submit.click(); } } // login.e2e.ts: tagged, traceable, runs on both it('LOGIN-004 @smoke signs in with MFA', async () => { await loginScreen.signIn(users.withMfa); await mfaScreen.enterCode(await otp.latest()); await expect(homeScreen.greeting).toBeDisplayed(); });
This is the shape of what you own after handover: readable, reviewed code, not a recorder’s output.
Performance testing with k6, AI-assisted
Most teams skip load testing because it means a second suite in a second tool. We build it from the journeys you already automate, in TypeScript, in the same repository and pipeline.
Peak Readiness Check
2 weeks · before a launch or peak season · fixed price
- Week 1We agree your forecast peak with your business and platform owners, and set pass-fail thresholds per journey. Your critical Playwright journeys and captured API traffic become k6 scripts through Grafana’s official k6 MCP server, reviewed by us before they run.
- Week 2Baseline, peak, stress, and soak runs against a production-like environment, a findings readout naming the slowest endpoints and the load where errors start, and the suite wired into CI as a regression gate.
Four runs, four questions
What each load profile tells you
- Baseline: how fast is it today, at normal traffic?
- Peak: does it hold at your forecast busiest hour, such as launch day, month-end, or a campaign?
- Stress: where does it break, and does it recover cleanly afterward?
- Soak: does it stay healthy over hours, or slowly leak memory and connections?
Where AI helps, and where it doesn’t
Scripts from your functional tests
The k6 MCP server converts Playwright journeys to k6 and validates each script against k6’s own documentation and type definitions before anything runs.
Results explained, not just charted
AI summarizes each run against the last one: which endpoint slowed down, at what load, and which recent change touched it.
Thresholds stay human decisions
Pass-fail limits come from your service-level targets and business owners, written into the code. AI never loosens a threshold to make a run pass.
A peak test, with its pass-fail gate in code
// checkout.peak.ts: 2x forecast peak, fails CI if breached import http from 'k6/http'; import { check } from 'k6'; export const options = { stages: [ { duration: '5m', target: 200 }, // ramp to forecast peak { duration: '15m', target: 400 }, // hold at 2x peak { duration: '5m', target: 0 }, ], thresholds: { 'http_req_failed': ['rate<0.01'], // under 1% errors 'http_req_duration{step:pay}': ['p(95)<800'], }, }; export default function () { const res = http.post(`${BASE}/api/checkout`, cart(), { tags: { step: 'pay' } }); check(res, { 'order created': (r) => r.status === 201 }); }
Readable TypeScript your engineers can own, with results in Grafana or your existing dashboards.
Deep and wide
Every application tested from the screen to the data, and every business journey tested across the systems it touches.
Vertical depth
Each application is tested top to bottom: the screens users see, the APIs and services behind them, and the data they write, so a passing UI never hides a broken record.
Horizontal, end-to-end journeys
Business flows are tested the way customers experience them, across applications, integrations, and API gateways, like an application submitted online that has to land correctly in Salesforce and downstream systems.
Regression that fits the release
Tiered suites, from smoke to targeted to full regression, with risk-based selection that runs the right tests for each change instead of everything, every time.
Test cases managed as an asset
One source of truth for test cases, organized by feature and by business flow, traceable to requirements, versioned, deduplicated, and retired when they no longer earn their place.
Let’s talk about your applications.
Tell us what you’re shipping and where testing slows you down.