Autonomous discovery of every user flow.
Point Clewly at a URL and a test account. Agents traverse the application, map its flows and propose a scenario for each one, with no recorder and no scripting.
Autonomous end‑to‑end testing for every release.
Clewly’s AI agents explore your web application, generate test coverage and run it in real browsers on your schedule. Your engineers get reproducible defects with steps, evidence and a full replay, without writing a single test script.
High · New#41 · /cart · 02:11
SAVE20 takes 12% off, not 20%
Expected$960.00Actual$1,056.00
Follow the thread
0 flows mapped
0 scenarios proposed
Human approval before anything runs.
Your team reviews proposed coverage and approves what matters. Checks based on assumptions rather than observable behaviour are flagged, keeping false positives out of your pipeline.
Suggested scenariosReview queue · 5
- Apply SAVE20 and check the 20% discountCart · Coupon
- Search finds “laptop” in any letter caseCatalog · Search
- Quantity can’t exceed the stockStock levels aren’t shown anywhere on the siteAssumption
- Checkout rejects an invalid emailCheckout · Forms
- Profile name is saved after reloadAccount · Profile
0 selectedApprove selected
4 scenarios approved. They join tonight’s run at 02:00.
Parallel execution in isolated real browsers.
Approved scenarios run on your schedule or on demand, each in its own sandboxed browser. Every action is recorded.
High · New#41 · /cart · 02:11:38
SAVE20 takes 12% off, not 20%
Expected$960.00Actual$1,056.00
23 passed · 1 defectrun finished 02:16:04
Every defect arrives ready to fix.
Each finding carries the expected and actual result, annotated screenshots and a step-by-step replay, so engineers can reproduce it without leaving the report.
- Expected vs. actual, in plain language
- A screenshot for every step, the failure marked
- Replay the run exactly as it happened
The screen your team opens every morning.
Clewly leads with where the release stands: which areas are failing, what changed overnight and what is waiting for a decision. Every number opens the evidence behind it.
123
123
- 1The state of the release in one sentence, not a wall of charts.
- 2Findings grouped by product area and root cause, worst first.
- 3Questions from the agent wait here until someone answers.
- 1Every square is a scenario, grouped by area. Gaps in coverage are visible.
- 2New suggestions sit in their own queue until approved.
- 3Each scenario keeps its recent run history, so flaky and new failures stand apart.
Rigorous where it matters. Transparent about uncertainty.
Targeted exploration.
Direct agents at a specific area, such as checkout or a newly released feature, and they go deep: every business rule, boundary condition and error path. Ideal before a release or after an incident.
focus
Clarifies instead of guessing.
When a test depends on information the application doesn’t expose, such as valid coupon codes or a second user role, the agent requests it. Answers become project context and apply to every subsequent run.
? Which coupon codes should be tested? None are listed on the site.
Root-cause deduplication.
The same failure across the cart, order history and checkout becomes one issue with three occurrences, not three tickets. It closes automatically when a passing run confirms the fix.
/cart · /orders/1042 · /checkout → #41
Locale-aware testing.
Reports in English or Turkish, per project. Test data follows the application’s locale, so a Turkish storefront is exercised with “İstanbul” and “Şükrü”, not ASCII placeholders.
Şehir: İstanbul · Ad: Şükrü Öztürk
Built to run against your staging environment.
- Isolated execution.
- Every run executes in its own locked-down container. Egress is limited to standard web ports, and internal network addresses are blocked.
- Credential isolation.
- Model requests are brokered through our gateway. Provider keys never enter the execution sandbox, and every job runs under a hard spend limit.
- Zero data retention.
- Requests are routed exclusively to model providers with zero data retention. Your data is never used for training.
- Encrypted secrets.
- Test-account credentials are encrypted at rest and never displayed after entry.
Questions teams ask before they connect.
Straight answers on setup, scope and data handling.
How long does setup take?
Add a staging URL and a test account. Exploration starts right away, and proposed scenarios are ready for review when it finishes. Nothing is installed in your codebase or your CI.
Which applications are supported?
Web applications reachable over standard web ports: storefronts, SaaS products, internal tools behind a login. No SDK, browser extension or code changes. Native mobile apps are not supported.
Do we write or maintain test scripts?
No. Scenarios are written in plain language and carried out by an agent in a real browser. They describe what a user does, not CSS selectors, so routine interface changes don’t break them.
Can it run against production?
Clewly is built for staging. Agents place orders, submit forms and create accounts, so point it at an environment where that is safe.
Where is our data processed?
Each run executes in an isolated container. Model requests go only to providers with zero data retention, and your data is never used for training. Test-account credentials are encrypted at rest.
Ship every release with confidence.
Connect your staging environment and review your first findings after the next run.