The AI that watches how people actually use your app - and writes the tests you kept meaning to write.
Here is a durable fact about software engineering: everybody agrees that tests are important, and almost nobody writes enough of them. The reason is not laziness. The reason is that writing an end-to-end test is tedious, maintaining it is worse, and the payoff - a build that stays green - is invisible right up until the moment it isn't. So tests are the first thing cut when a deadline looms, which means the codebase is least protected exactly when it is changing fastest. This is a bad equilibrium, and Checksum.ai's entire pitch is that artificial intelligence can break it.
Checksum is a San Francisco company, founded in 2022, that uses AI agents to write and maintain end-to-end tests for web applications. The important word there is maintain. Plenty of tools can spit out a test script. The expensive, soul-eroding part of quality assurance is not the writing - it's the upkeep. A designer nudges a button three pixels to the left, a developer renames a field, and suddenly fifty tests fail, none of them because of a real bug. Someone has to go fix the selectors. That someone is usually a skilled engineer doing work that feels a lot like data entry.
Checksum's answer is to learn from how real users actually move through your product. Its agents observe genuine user sessions, infer the flows that matter, and generate production-ready tests in Playwright or Cypress - the two open-source frameworks most teams already use. When the application changes and a test breaks, the system proposes a fix and routes it through your normal pull-request review, so a human still gets to say yes. You get coverage that heals itself, and you keep the receipts.
"Checksum automatically generates and maintains end-to-end tests based on user sessions, so you can move fast without breaking things."
Checksum has organized itself around specialized AI agents, each aimed at a different corner of the coverage gap. The framing is tidy, and it maps to how testing actually fails in the real world - which is to say, in several places at once.
Generates Playwright tests from real user sessions and automatically fixes them when the UI or flows change - fixes you approve in a PR, not a black box.
Creates 50-200 targeted tests per pull request, so a code change is exercised before it merges rather than after it breaks.
Extends automated coverage to the API layer, reportedly covering thousands of endpoints within days.
Monitors production and converts real errors into new regression tests - so the same failure doesn't get to happen twice.
There is a subtle but important design choice buried in all of this: Checksum hands you the test code. Many testing platforms keep your tests inside their own proprietary system, which is great for the vendor and less great for you, because leaving means starting over. Checksum delivers standard Playwright and Cypress - code you own outright. It is a small decision with a large implication, which is that trust is easier to earn when the exit door is always unlocked. If the product stops earning its keep, you walk away with everything it built.
"Tests are delivered as real code - no vendor lock-in."
*Company- and customer-reported figures. Treat as approximate.
Numbers below are self- and customer-reported, so read them the way you'd read any vendor's case study - directionally, with a raised eyebrow. Still, the shape is consistent: the value is measured in engineer-hours that stop being spent on manual clicking.
Vered's background reads like a random-number generator with a taste for hard problems: an Israeli Navy officer, then a product manager at Google, then a CTO at a Y Combinator-backed company, with a Kellogg MBA somewhere in between. The connective tissue is a low tolerance for skilled people doing repetitive work. Checksum was built inside super{set}, a San Francisco data-and-AI startup studio, which shows in the company's discipline - one painful, universal problem, attacked directly, rather than a sprawling roadmap.
There is a nice irony in the timing. The last few years handed developers a fleet of AI coding assistants - Copilot, Cursor, Claude - that let them write and ship code dramatically faster. Checksum's own team reportedly uses them too. But shipping faster also means shipping bugs faster, and the traditional way to catch bugs - careful, hand-written tests - is exactly the slow, manual thing that didn't get an AI upgrade. Checksum sits precisely in that gap. If your engineers adopted AI to write code this year, the honest question is whether anything kept your test coverage from falling behind.
Checksum has not been especially loud about its balance sheet, and the third-party numbers vary, so here is the picture with appropriate hedging. It is a small company that appears to punch above its headcount - which is roughly the whole thesis of an AI-native startup.
| Metric | Reported figure |
|---|---|
| Stage | Seed |
| Total funding | ~$750K (reported) |
| Backers | super{set} studio; LEAP Global Partners |
| Revenue | ~$2M ARR (third-party est., 2025) |
| Valuation | ~$5.9M (third-party est.) |
| Team | ~18-28 people |
Figures compiled from getLatka, Crunchbase, Tracxn and the provided profile. Sources disagree; treat as approximate.
Zoom out and the competitive landscape is a crowded, energetic corner of dev tools. Checksum shares the field with the likes of QA Wolf, Reflect, Testim, mabl, Autify and Rainforest QA, plus the incumbent that never goes away entirely: a room full of humans clicking through a regression checklist by hand. What distinguishes Checksum's approach is the insistence on learning from real usage rather than asking you to describe your app in advance, and the commitment to handing back ownable code. Neither is a magic moat, but together they describe a coherent bet - that the winning testing tool is the one that removes the most drudgery while asking for the least trust up front.
None of this makes bugs disappear. AI-generated tests still need human judgment about what "correct" means, and an auto-healed test can happily paper over a real regression if nobody is paying attention - which is why the pull-request checkpoint matters so much. But the core insight is sound and a little bit humbling: most of what we call "testing work" was never the interesting part. It was the tax we paid for the interesting part. Checksum is a company built on the premise that the tax can be automated, and that if you make coverage nearly free, teams will stop treating it as optional. Whether that holds at scale is the open question. For now, it is a genuinely useful bet on the unglamorous plumbing that keeps software from breaking - which, if you have ever been paged at 3 a.m., you know is not unglamorous at all.
Profile compiled from public sources including checksum.ai, LinkedIn, Crunchbase, getLatka, Tracxn and super{set}. Financial and performance figures are company- or third-party-reported and should be treated as approximate. Sources occasionally disagree; where they do, ranges are shown.
Checksum.ai is an AI software-testing company that automatically writes and maintains end-to-end tests for web applications. Its AI agents learn from real user sessions to generate production-ready Playwright and Cypress tests, then auto-heal them when the app changes - so engineering teams get broad test coverage in days instead of months and stop babysitting flaky test suites. Founded in 2022 and built inside the super{set} startup studio, the San Francisco company serves developers and QA teams who want to ship fast without breaking things.
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