Mobile app device test automation workbench

For

Mobile engineering teams shipping iOS and Android apps

Solves

Manual device testing and brittle test scripts slow releases and miss bugs that only appear on real hardware.

Delivers

Reviewed test runs with artifacts, crash reports and merge gates

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce regression time and escaped defects while keeping test ownership in the team.

1. Run tests on actual iOS and Android devices.
2. Run tests on emulators as an alternative.
3. Define tests in plain-English descriptions.
4. Generate test scripts from descriptions or app context.
5. Adapt tests to UI changes and dynamic elements.
6. Run many tests simultaneously.
7. Embed testing into CI/CD pipelines.
8. Connect directly to code repositories.
9. Integrate with code editors for earlier testing.
10. Capture crashes and performance metrics.
11. Show network requests and responses during a session.
12. Compare app state and file-system changes across runs.
13. Map all screens and flows automatically.
14. Generate rich bug reports and share results.
15. Provide video recordings, logs and detailed reports.
16. Trigger cloud device runs from terminal or CI and gate merges.
17. Catch issues early with immediate feedback.
18. Support multi-account and landscape mode scenarios.

Everything these tools do, in one app

• Real device testing Run tests on actual iOS and Android devices for accurate environment simulation.Found in NativeBridge, QualGent, QualGent AI and 1 more
• Emulator support Run tests on emulators as an alternative to physical devices.Found in NativeBridge, QualGent
• Natural language test creation Define tests using plain-English descriptions instead of writing code.Found in NativeBridge, Autosana, QualGent and 1 more
• AI test generation Automatically generate test scripts from descriptions or app context.Found in NativeBridge, Autosana, QualGent and 1 more
• Self-healing tests Automatically adapt tests to UI changes and dynamic elements to reduce maintenance.Found in QualGent, QualGent AI
• Parallel test execution Run many tests simultaneously to speed up regression cycles.Found in QualGent
• CI/CD integration Embed testing into continuous integration and deployment pipelines.Found in Autosana, QualGent, Revyl
• Code repository integration Connect directly to code repositories for seamless workflow integration.Found in Autosana
• Editor integrations Integrate with code editors for earlier-stage testing.Found in NativeBridge
• Crash and performance reporting Capture crashes and performance metrics like CPU, memory, and FPS.Found in NativeBridge, Revyl
• Network waterfall Show all network requests and responses during a test session.Found in Revyl
• State and file-system diffing Compare app state and file system changes across runs to catch accumulating issues.Found in Revyl
• Screen and flow mapping Automatically map all screens and flows in the app to keep documentation current.Found in Revyl
• Bug reporting and sharing Generate rich bug reports and share results via links or direct notifications.Found in NativeBridge, Autosana, QualGent and 1 more
• Test artifacts Provide video recordings, logs, and detailed reports for each test run.Found in QualGent AI, Revyl
• CLI and GitHub Action Trigger cloud device runs from the terminal or CI and gate merges based on test results.Found in Revyl
• Real-time feedback Catch issues early with immediate feedback loops.Found in Autosana
• Multi-account and landscape mode Support testing scenarios with multiple accounts and landscape orientation.Found in QualGent

What goes in, what comes out

The workflow

1. In
   Start with

   App builds, plain-English test descriptions, device profiles and CI settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect app builds

4. 3

   Plain-English test descriptions

5. 4

   Device profiles and CI settings

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed test runs with artifacts, crash reports and merge gates

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed app build and device matrix; final release decisions and bug triage remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Test authoring and device selection, Live run and artifact review, CI gate and release report. Use a thumbnail gallery for test suites, a large central run canvas, and a right-hand panel for device profiles, logs and comments. Let users compare runs side by side. Display draft, running, passed and failed states. Provide a shareable run link with comments anchored to the relevant step. Make the task-specific outcome reviewed test runs with artifacts, crash reports and merge gates visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, build versions, device allocations, team comments, approval states, usage allowances, run limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

App-owned repositories, authorized CI systems and permitted device clouds. Cloud device storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   7 days

   One buyer segment, one recurring use case; first modules: run tests on actual iOS and Android devices; run tests on emulators as an alternative. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will mobile engineering teams shipping iOS and Android apps use it to solve "manual device testing and brittle test scripts slow releases and miss bugs that only appear on real hardware"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Regression cycle time and escaped defects per release.
4. Measure, then decide. Track regression cycle time and escaped defects per release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One fixed app build and device matrix; final release decisions and bug triage remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: run tests on actual iOS and Android devices; run tests on emulators as an alternative. Support the third module with operator review: define tests in plain-English descriptions. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed test runs with artifacts, crash reports and merge gates. Retain the explicit scope boundary: One fixed app build and device matrix; final release decisions and bug triage remain engineering.

What the build depends on. App upload and preview, asynchronous test jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed app build and device matrix; final release decisions and bug triage remain engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined app package. Offer a monthly production allowance after repeat demand. Quote complex device matrices or specialist QA separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed test runs with artifacts, crash reports and merge gates. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce regression time and escaped defects while keeping test ownership in the team. Demonstrate a concrete reviewed test runs with artifacts, crash reports and merge gates using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Mobile engineering teams shipping iOS and Android apps professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed test runs with artifacts, crash reports and merge gates from a small authorized input set, with a transparent calculation of regression cycle time and escaped defects per release and no promised savings.

The first 30 days

1. Week 1: interview five mobile engineering teams shipping iOS and Android apps and inspect a recent example of manual device testing and brittle test scripts slow releases and miss bugs that only appear on real hardware.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure regression cycle time and escaped defects per release, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Regression cycle time and escaped defects per release. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Regression cycle time and escaped defects per release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed test runs with artifacts, crash reports and merge gates. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved test patterns, device profiles and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for mobile engineering teams shipping iOS and Android apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

NativeBridge, Autosana, QualGent, QualGent AI and Revyl, plus manual QA and in-house device labs. Compare this product with the buyer's present method on regression cycle time and escaped defects per release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Device cloud time, emulator processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed test runs with artifacts, crash reports and merge gates. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve code ownership, source attribution, test accuracy and usage permissions. Engineering leads approve substantive changes and release scope. One fixed app build and device matrix; final release decisions and bug triage remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.