Plain-language test generation and execution workspace

For

QA leads and engineering teams shipping web, API and mobile software

Solves

Test suites are written and maintained by hand, so coverage lags behind releases and UI changes break scripts.

Delivers

Reviewed, runnable test suites with pass/fail evidence

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

Cut manual test authoring and maintenance while keeping release checks under team control.

1. Accept plain-language test scenarios.
2. Generate runnable test cases from descriptions or by learning the application.
3. Support no-code authoring and Gherkin input.
4. Execute tests automatically across web, API, mobile and stress scenarios.
5. Run tests in parallel and on a schedule.
6. Cover multiple browsers, screen sizes, real devices and emulators.
7. Test chatbot dialogs and Salesforce interfaces.
8. Self-heal tests when the UI changes.
9. Compare the reviewed result with the recorded baseline and value assumptions.
10. Capture corrections and named-owner approval before consequential use.
11. Report failures, inconsistencies and coverage gaps.
12. Export a versioned reviewed, runnable test suites with pass/fail evidence with source references and unresolved questions.

Everything these tools do, in one app

• Natural language test creation Lets users describe test scenarios in plain English instead of writing code.Found in Qagent, QA.tech, QAClan and 3 more
• AI-generated test cases Automatically creates runnable test cases from user descriptions or by learning the application.Found in Qagent, QA.tech, QAClan and 5 more
• No-code test authoring Enables test creation without manual scripting or deep technical expertise.Found in Qagent, QAClan, Qodex.ai and 3 more
• Automated test execution Runs generated tests automatically to check application behavior.Found in Qagent, QA.tech, QAClan and 6 more
• CI/CD integration Connects automated tests into development pipelines for continuous validation.Found in Qagent, QA.tech, QAClan and 5 more
• Self-healing tests Automatically adapts tests when the application UI changes to reduce maintenance.Found in QAClan, Qodex.ai, ACCELQ and 1 more
• Test scheduling Runs tests automatically on a schedule to catch issues early.Found in Qagent, AutoFlow Studio
• Multi-browser and screen testing Checks applications across different browsers and screen sizes.Found in Qagent
• Web, API, and stress testing Supports testing of web interfaces, APIs, and stress scenarios in one platform.Found in QAClan
• API testing focus Automatically creates and runs tests specifically for APIs.Found in Qodex.ai
• Visual test management Shows test cases as flowcharts for easy understanding and organization.Found in AutoFlow Studio
• Parallel test execution Runs multiple tests at the same time to speed up results.Found in AutoFlow Studio, TestZeus
• Mobile and cross-platform testing Tests applications on mobile devices and other platforms.Found in ACCELQ, Drizz, TestZeus
• Chatbot dialog testing Automates testing of chatbot conversations and interactions.Found in bottest.ai
• Test reporting Provides detailed reports on test results, failures, and inconsistencies.Found in bottest.ai
• Real device execution Runs tests on actual mobile devices and emulators using visual AI.Found in Drizz
• Salesforce-specific testing Uses Salesforce's UI API to create dynamic locators and test Salesforce applications.Found in TestZeus
• Gherkin test input Allows tests to be written in Gherkin format for standardized execution.Found in TestZeus

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language scenarios, application access and pipeline hooks

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language scenarios

4. 3

   Application access and pipeline hooks

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed, runnable test suites with pass/fail evidence

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 application under test and one pipeline; release sign-off and defect triage remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Scenario intake and application access, Editable test workspace, Run results and release evidence. Use a project list for applications, a central canvas showing test cases as flowcharts, and a right-hand panel for steps, locators, data and comments. Let users compare runs side by side. Display draft, changes requested and approved states. Provide a pipeline view with run history and a shareable results link. Make the task-specific outcome reviewed, runnable test suites with pass/fail evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, environment credentials, test versions, reviewer comments, approval states, run quotas, retention 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

Customer-owned repositories, test environments and CI/CD pipelines. Cloud test runners, issue trackers and reporting destinations. Start with file exchange and validate destination specifications before promising direct pipeline writes. 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: accept plain-language test scenarios; generate runnable test cases from descriptions or by learning the application. 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 QA leads and engineering teams shipping web, API and mobile software use it to solve "test suites are written and maintained by hand, so coverage lags behind releases and UI changes break scripts"?
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: Accepted test cases per QA hour and maintenance hours per release.
4. Measure, then decide. Track accepted test cases per QA hour and maintenance hours 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 application under test and one pipeline; release sign-off and defect triage remain human. Implement one approved input format, a bounded representative case set and the first two task modules: accept plain-language test scenarios; generate runnable test cases from descriptions or by learning the application. Support the third module with operator review: execute tests automatically across web, API, mobile and stress scenarios. 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, runnable test suites with pass/fail evidence. Retain the explicit scope boundary: One application under test and one pipeline; release sign-off and defect triage remain human.

What the build depends on. Application access and preview, asynchronous test jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist QA review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One application under test and one pipeline; release sign-off and defect triage remain human.

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 application package. Offer a monthly production allowance after repeat demand. Quote complex mobile, stress or Salesforce coverage separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, runnable test suites with pass/fail evidence. 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

Cut manual test authoring and maintenance while keeping release checks under team control. Demonstrate a concrete reviewed, runnable test suites with pass/fail evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

QA leads and engineering teams shipping web, API and mobile software professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, runnable test suites with pass/fail evidence from a small authorized input set, with a transparent calculation of accepted test cases per QA hour and maintenance hours per release and no promised savings.

The first 30 days

1. Week 1: interview five QA leads and engineering teams shipping web, API and mobile software and inspect a recent example of test suites written and maintained by hand, so coverage lags behind releases and UI changes break scripts.
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 accepted test cases per QA hour and maintenance hours 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: Accepted test cases per QA hour and maintenance hours 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

Accepted test cases per QA hour and maintenance hours 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, runnable test suites with pass/fail evidence. 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, application quirks 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 QA leads and engineering teams shipping web, API and mobile software. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Qagent, QA.tech, QAClan, Qodex.ai, AutoFlow Studio, ACCELQ, bottest.ai, Drizz and TestZeus. Compare this product with the buyer's present method on accepted test cases per QA hour and maintenance hours per release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model and execution attempts, device and browser farm usage, storage, reviewer hours, client revision rounds and licensed test data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, runnable test suites with pass/fail evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve test data privacy, source attribution, environment permissions and usage rights. QA owners approve test scope and release decisions. One application under test and one pipeline; release sign-off and defect triage remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.