Ship faster with AI

Checksum's agents validate every change at every stage of your development lifecycle, automatically. See how teams are moving from prompt to production without quality becoming the bottleneck.

Continuous quality from prompt to production.

End-to-End Agent
Generate, run, and heal UI tests automatically. Your suite stays green as your product evolves.
CI Agent
Get targeted test coverage on every PR, specific to the code that changed, inside your existing pipeline..
API Agent
Cover every endpoint, parameter, and payload variation with tests that evolve as your API changes.

Helping Companies Ship 10X Faster

The challenge

Your suite breaks more than your code.

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Your E2E suite fails for reasons unrelated to your feature. Selectors change, flows shift, timing breaks. You spend hours debugging failures that have nothing to do with the code you shipped. Meanwhile, PRs pile up and your actual work waits. Test maintenance has become a second job, and it is one nobody signed up for.

The frustrating part is that the suite exists for good reason. You need that safety net. But when it cries wolf often enough, engineers stop paying attention. Failures get dismissed. Real regressions slip through because the signal is buried in noise. You end up in a worse position than if the suite did not exist at all: slower to ship, no more confident, and now responsible for keeping a flaky test infrastructure alive on top of everything else.

What changes with Checksum

Outcome-focused quality that works in the background, so you don't have to.

Self healing

Tests that fix themselves. When the UI evolves, Checksum automatically heals broken tests and opens a PR for your review. No more selector archaeology.

Tested before review

Feedback before review CI Agent runs 50-200 targeted tests on every PR. By the time someone reviews your code, it's already been executed and verified.

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Reliable signal

A suite you can trust again 82% lower failure rates vs. manual maintenance. Fewer false alarms. Clearer signal when something actually breaks.

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Real Customer Outcomes

Trusted by fast shipping teams

Financial Tech

90

hours saved on manual testing per month, 0 issues missed by Checksum, Full E2E coverage in weeks
How Söderberg & Partners Replaced Manual Release Testing With a Fully Automated E2E Suite
Learn more →
Manual testing made releases painful, so we couldn't deploy as often as we wanted. With Checksum, we have full coverage, we trust the releases, and we can focus on more important stuff than writing boring tests.
AI Lead, Söderberg & Partners
Insurance Tech

1

issue caught per week, Zero production outages, 10x QA team impact
How Checksum helps Counterpart ships to production every day, without outages
Learn more →
Checksum gives me the impact of a full QA team for less than half the monthly salary of an offshore developer. Why would I not spend that money?
Engineering Manager
Legal Tech

30%

faster engineering cycles. 70% fewer bugs. 0% flakiness.
How Postilize achieved full regression testing with Checksum
Learn more →
With Checksum, Postilize is able to simply ship faster. Having a full testing suite with no flakes and little effort on our side allows us to spend less time firefighting, get immediate feedback, and ship to production daily.
Co-founder, Postilize
Travel Tech

$200K

saved annually. 1 month to full test suite. 20% engineering time reclaimed.
How Reservamos Saved $200K a Year by Automating QA Across Every Client Environment
Learn more →
Checksum saved us $200K a year. The fact that they provide a comprehensive testing suite and maintain it in real-time is a game changer. Our engineering team moves and innovates faster and paying per test allows us to tie the costs directly to the money saved.
CTO, Reservamos
SaaS

$500K

annual savings, 6 critical bugs caught weekly, 250+ end-to-end tests built in under a month
How Clearpoint Strategy Built 250 E2E Tests in a Month, and Stopped 6 Critical Bugs a Week
Learn more →
Checksum is a game-changer. It saves me so much time writing tests so I can deploy my engineering resources to building tomorrow's technology today—not fixing yesterday's release over and over again.
Co-founder, Clearpoint Strategy
SaaS

200+

full user-journey E2E tests built and managed
How Ketch Scaled to Nearly 200 E2E Tests Without Building an Automation Team
Learn more →
Checksum turned end-to-end testing into a reliable release signal for us. The suite runs daily, stays healthy as our product evolves, and helps us ship with confidence.
Co-founder, Head of Product, Ketch
Ed Tech

40%

reduction in manual testing time
How Stellic cut manual testing by 40% with Checksum’s AI-powered E2E testing
Learn more →
Checksum provides more than just AI-driven testing; they provide peace of mind. We no longer worry about broken tests or lengthy testing cycles. Instead, we can focus on scaling our platform and delivering value to our customers.
Head of Engineering, Stellic
Retail

500

faster launch, 200 hours saved
How Engagement Agents migrated 500 tests in a week and launched their UI redesign 30% faster
Learn more →
Checksum turned what looked like a months-long test rewrite into a one-week migration. When our new UI landed, all the tests were already green and we launched 30% faster, with full confidence.
Founder and President, Engagement Agents

Frequently Asked Questions

Coding agents write tests when you ask them to. Checksum runs continuously in the background, generating, executing, and healing tests automatically without anyone prompting it. The difference is on-demand versus always-on. Most teams find they're spending more time fixing AI-generated tests than writing them. Checksum removes that loop entirely.

No. It removes the low-leverage work, writing and maintaining tests that break every time the UI changes. QA teams that use Checksum spend less time on upkeep and more time on exploratory testing, edge cases, and quality strategy.

Most teams are running their first tests within a day. Checksum connects to your existing CI pipeline and works with your current frameworks. There's no rip-and-replace.

Yes. Tests are delivered as real code: Playwright for end-to-end tests that lives in your repository. You can run them anywhere, modify them however you want, and take them with you. No vendor lock-in.

When a selector changes or a flow shifts, Checksum detects the failure, fixes the test, and opens a PR for your review. You see exactly what changed and can approve or reject it. About 70% of failures resolve this way without any human involvement.

Checksum works alongside what you already have, not instead of it. It fills gaps in coverage, keeps existing tests green, and generates new tests as your product changes.

Most teams start by reviewing everything, then gradually extend trust as they see the results. You always have controls: tests come as PRs, healing changes are reviewable, and you can adjust scope at any time.

General

Creates production-ready Playwright tests. When your app evolves, the agent automatically heals broken tests.

E2E Agent

Creates production-ready Playwright tests. When your app evolves, the agent automatically heals broken tests.

CI Agent

Creates production-ready Playwright tests. When your app evolves, the agent automatically heals broken tests.

API Agent

Creates production-ready Playwright tests. When your app evolves, the agent automatically heals broken tests.

Ship faster with confidence

Ship fast because your testing suite has your back. Full test coverage from day one.

Intelligent testing agents

Three specialized agents working together to keep your codebase fully tested.

End-to-end tests

Creates production-ready Playwright tests. When your app evolves, the agent automatically heals broken tests.

Learn more →

CI Guard

Generates 50-200 tests for each PR, targeting the exact code that changed. By the time you review a PR, it's already been executed and verified.

Learn more →

API testing

Covers thousands of endpoints in days, not months. Tests span multiple endpoints and verify your system actually works, not just status codes.

Learn more →

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Real Customer Outcomes

Trusted by fast shipping teams

Financial Tech

90

hours saved on manual testing per month, 0 issues missed by Checksum, Full E2E coverage in weeks
How Söderberg & Partners Replaced Manual Release Testing With a Fully Automated E2E Suite
Learn more →
Manual testing made releases painful, so we couldn't deploy as often as we wanted. With Checksum, we have full coverage, we trust the releases, and we can focus on more important stuff than writing boring tests.
AI Lead, Söderberg & Partners
Insurance Tech

1

issue caught per week, Zero production outages, 10x QA team impact
How Checksum helps Counterpart ships to production every day, without outages
Learn more →
Checksum gives me the impact of a full QA team for less than half the monthly salary of an offshore developer. Why would I not spend that money?
Engineering Manager
Legal Tech

30%

faster engineering cycles. 70% fewer bugs. 0% flakiness.
How Postilize achieved full regression testing with Checksum
Learn more →
With Checksum, Postilize is able to simply ship faster. Having a full testing suite with no flakes and little effort on our side allows us to spend less time firefighting, get immediate feedback, and ship to production daily.
Co-founder, Postilize
Travel Tech

$200K

saved annually. 1 month to full test suite. 20% engineering time reclaimed.
How Reservamos Saved $200K a Year by Automating QA Across Every Client Environment
Learn more →
Checksum saved us $200K a year. The fact that they provide a comprehensive testing suite and maintain it in real-time is a game changer. Our engineering team moves and innovates faster and paying per test allows us to tie the costs directly to the money saved.
CTO, Reservamos
SaaS

$500K

annual savings, 6 critical bugs caught weekly, 250+ end-to-end tests built in under a month
How Clearpoint Strategy Built 250 E2E Tests in a Month, and Stopped 6 Critical Bugs a Week
Learn more →
Checksum is a game-changer. It saves me so much time writing tests so I can deploy my engineering resources to building tomorrow's technology today—not fixing yesterday's release over and over again.
Co-founder, Clearpoint Strategy
SaaS

200+

full user-journey E2E tests built and managed
How Ketch Scaled to Nearly 200 E2E Tests Without Building an Automation Team
Learn more →
Checksum turned end-to-end testing into a reliable release signal for us. The suite runs daily, stays healthy as our product evolves, and helps us ship with confidence.
Co-founder, Head of Product, Ketch
Ed Tech

40%

reduction in manual testing time
How Stellic cut manual testing by 40% with Checksum’s AI-powered E2E testing
Learn more →
Checksum provides more than just AI-driven testing; they provide peace of mind. We no longer worry about broken tests or lengthy testing cycles. Instead, we can focus on scaling our platform and delivering value to our customers.
Head of Engineering, Stellic
Retail

500

faster launch, 200 hours saved
How Engagement Agents migrated 500 tests in a week and launched their UI redesign 30% faster
Learn more →
Checksum turned what looked like a months-long test rewrite into a one-week migration. When our new UI landed, all the tests were already green and we launched 30% faster, with full confidence.
Founder and President, Engagement Agents
Real Customer Outcomes

Trusted by fast shipping teams

The AI velocity paradox: Why faster code made testing harder

Generating tests was never the hard part. Keeping them green as the app changes is. How continuous quality closes the testing gap that AI coding opened up.
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Quality enablement is the new developer experience

The QA-to-QE shift isn't about renaming a role or shrinking a team. It's the platform engineering model applied to quality. Brittany Roberts on what she sees across her customer book at Checksum: how engineering orgs are reshaping QA into a strategic enablement function in 2026, and what the new team looks like.
Read blog

The Continuous Quality Agent is here. Watch it work.

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CI/CD has a third stage.

CI tells you the code merged. CD tells you it deployed. Neither one tells you if it still works the way your users experience it. As AI coding agents push shipping velocity higher, that gap between "tests passed" and "it actually works" keeps widening. This post makes the case for a third stage: Continuous Verification, the part of the pipeline that runs against your live system, tests real flows, and doesn't wait for a human to kick it off.
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API testing with AI isn't just Claude's job

AI test generators look great in demos — they read your OpenAPI spec and produce a full suite of passing tests in minutes. The problem is that passing tests and tests that catch bugs are two different things. Left to its own devices, an AI will systematically skip the hard tests: end-to-end CRUD flows, auth boundary checks, business invariants, and concurrency edge cases. It does this because those tests are harder to make pass, and the model is optimizing for clean output, not coverage. The result is a silent gap between what was proposed and what was shipped — invisible unless you specifically instrument for it. Closing that gap requires platform engineering, not prompt engineering: a multi-stage pipeline (planner → implementer → reviewer → verifier → healer), a coverage matrix that makes untested areas visible and challengeable, auth-boundary suites built into the planner by default, deterministic test execution, and a policy of shipping failed tests as explicit skipped entries rather than quietly dropping them. That last point — never filtering failures — is framed as the single most consequential design decision in the whole system. The post closes with a litmus test for any AI testing tool: "Show me a test you proposed and couldn't pass. How does it appear in the output?" If it disappears, the tool optimizes for demos. If it ships as a skipped test with a reason and a next step, it optimizes for finding bugs.
Read blog

The one thing AI makes harder

The faster coding agents ship, the harder it gets to know whether what's going out is actually right. Daniel Harkin, Senior Software Architect at Checksum, makes the case that trust is the one quality that doesn't scale automatically with velocity, and why using the same LLM to write code and generate tests is a structural problem, not a tooling one.
Read blog

The real cost of QA testing tools (it's not the license fee)

License fees are the easiest part of QA tooling to budget for. The harder costs are the engineering time spent fixing broken tests, coverage debt that quietly accumulates, implied headcount assumptions, which rarely show up in a vendor demo. This guide breaks down what to actually evaluate before you commit.
Read blog

Your coding agent can't test what it doesn't know

Coding agents like Claude Code can write tests, run them, and fix what breaks. But they operate in a bounded context, stop when the task ends, and leave someone else holding the maintenance bill. This post breaks down the three gaps between "verified the feature I built" and "quality coverage for a production app."
Read blog

Your team is shipping more code than ever. Do you trust it?

AI tools have made it easier than ever to write code. Shipping that code with confidence is a different problem entirely. In this post, we look at why the quality gap is widening as AI-generated code fills more of the surface area of modern software, and why the answer isn't more headcount. It's better infrastructure. We share how Counterpart, an insurance technology company shipping to production every day with no dedicated QA team, built the testing foundation that gives their engineering team a clear yes or no before every release.
Read blog

Results as a service: Why outcomes drive everything we build

AI testing tools have a dirty secret: they generate tests, then hand the maintenance problem back to you. Checksum argues that this "tool trap" is why most teams end up with a dedicated person just keeping tests from breaking. This post makes the case for Results as a Service, a delivery model where Checksum takes accountability for whether tests stay accurate and working as your product evolves. That means self-healing tests, autonomous failure resolution (70% of failures fixed without your team), and production-ready Playwright suites from day one, not a fragile pile of generated code. It also addresses why an AI-first company still has solutions engineers: not to do what AI can do, but to cover what it cannot, like judgment calls, context-setting, and knowing when a technically valid test is practically useless. The broader market is moving this direction too. Nearly half of SaaS companies are exploring outcome-based pricing models, and engineering buyers increasingly ask "will this make our releases safer?" over "does it have the features we need?" Customers like Clearpoint Strategy ($500K in annual savings) and Reservamos ($200K saved) show what that shift looks like in practice.
Read blog

Auto-recovery vs. auto-healing: Why the difference matters more than you think

Most testing tools claim automatic test repair, but what they deliver is auto-recovery: patching selectors when a button moves. Auto-healing is a fundamentally different capability, one that keeps your tests accurate when entire flows change. As AI accelerates feature output, the gap between the two is where engineering time goes to die.
Read blog

What autonomous software engineering actually requires

Autonomous software engineering is only half-built. Coding agents have solved the generation problem, but without a way to verify generated code against the full reality of a production environment, there is still a human bottleneck at the end of every workflow. This post introduces the Code World Model as the missing infrastructure layer, drawing on the autonomous vehicles analogy to explain why simulation is what makes true autonomy possible. It also addresses what this shift means for engineering org design: what humans will focus on when generation and verification are both automated, and why building that infrastructure now is a structural bet worth making.
Read blog

The prompt-test-prompt loop is killing your day

AI coding tools have created a new time sink for developers: the prompt-test-prompt loop. Tests break from selector brittleness and context blindness, and manual debugging eats hours that should go to shipping. This post breaks down why the problem is structural, what self-healing tests actually do (and don't do), and how a continuous quality layer replaces the debug loop with automated verification that catches failures before you have to find them yourself.
Read blog

Why the AI productivity promise doesn't add up

AI coding tools are shipping more code than ever, but AI-generated code contains 1.7x more errors and code review time has jumped 93%. Learn why the math of AI-accelerated teams is broken, and how continuous testing and automated software testing close the verification gap so engineering teams can ship fast without falling further behind.
Read blog

Checksum AI and Google Cloud: End-to-end testing AI innovation

Checksum Is Now Available on Google Cloud Marketplace Checksum has graduated from the Google Cloud Emerging Partner Springboard Program and is now available on Google Cloud Marketplace. AI-powered end-to-end testing is now easier to deploy, procure, and scale within your existing Google Cloud environment.
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Why we built a system of AI agents to automate E2E testing

Why Checksum Uses a System of LLM Agents Instead of One Large Model A single general-purpose model is not the best way to build reliable AI testing. Here is how Checksum orchestrates an array of smaller, specialized models to improve accuracy, reduce hallucinations, and generate end-to-end tests faster.
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Three stages of technology transformation

The Three Stages of Technology Transformation: Where LLMs and Testing Are Headed From faster test generation to real-time autonomous maintenance, LLMs are reshaping how software gets tested. Here is the mental model Checksum uses to think about where this technology is going next.
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Flaky tests are costing you more than you think: Here’s how to fix It

How Checksum's Auto-Recovery Keeps Tests Running When Your UI Changes Flaky tests slow teams down and erode confidence in automation. Here is how Checksum's AI-driven auto-recovery detects unexpected UI changes, adapts in real time, and keeps your test suite running without false failures.
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Autonomous SDLC: A test product perspective with modern software

Checksum CEO Gal Vered on Autonomous Testing and the Future of the SDLC Checksum co-founder Gal Vered joins Modern Software's Mike Verinder to discuss how AI is reshaping end-to-end testing, why quality is the missing layer in autonomous engineering, and what the future of the SDLC actually looks like.
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Your Gen AI app is growing. Your test coverage Isn’t.

Why GenAI Teams Need a Different Approach to QA Shipping daily with brittle test scripts and manual regression cycles is not sustainable. Here is how AI-native teams are replacing outdated QA frameworks with fully managed, self-healing test automation that scales with their product.
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Does output format actually matter? An experiment comparing JSON, XML, and Markdown for LLM tasks.

Does Output Format Matter for LLM Tasks? We Tested JSON, XML, and Markdown We ran 90 experiments across coding, bug fixing, and creative writing tasks to find out if output format affects LLM performance. The short answer: less than you'd think. Here's what we found.
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New in Checksum: Faster quality signals across CI/CD

What's New in Checksum: Feature Health Dashboard, Ticketing Integrations, and Smarter Triage Checksum's latest updates give engineering teams a nightly health snapshot, on-demand test runs, and automatic bug routing into Jira, Linear, and Slack so quality signal becomes actionable work faster.
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No code test automation

What Is No-Code Test Automation and How Does It Work? No-code test automation lets teams create, run, and maintain tests in plain English without writing a single line of code. Here is how AI-powered platforms like Checksum generate production-quality Playwright tests from natural language descriptions.
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The true cost of maintaining a test suite

The True Cost of Maintaining a Test Suite Test maintenance is invisible until it isn't. Learn how to calculate what your team is actually spending on failures, where the time goes, and how AI-assisted maintenance reduces that cost by up to 99%.
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Flaky tests: why they happen and how to cut failures fast

Why Flaky Tests Happen and How to Fix Them Flaky tests are not random. Selector changes, flow drift, environment instability, and timing issues account for most failures. Here is how to diagnose what is breaking and build a maintenance loop that keeps your test suite reliable.
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The problem with web agent benchmarks (and why we need better ones)

Why AI Browser Automation Benchmarks Are Measuring the Wrong Thing Aggregate accuracy scores don't tell you which workflows you can actually automate. Here's why production AI automation depends on agent harnesses, code-based healing, and resilience over time, not one-shot benchmark performance.
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Repo Mirror: Ending the drift between code and UI

Bidirectional GitHub Sync for Checksum: Repo Mirror keeps your GitHub repository and Checksum UI automatically in sync. No manual exports, no drift. Developers stay in their IDE, QA works in the dashboard, and your code is always the source of truth.
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Continuous Quality: Building a world model for software

AI can write code in seconds. Deploying it with confidence still takes days. We explore why coding agents can't see what happens when their code hits production, and how a Code World Model closes that gap.
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47% better: What happened when we stopped teaching our agent our stack

We changed how our AI agent works—no new model, no new data—and improved end-to-end test quality by 47%. Here’s why letting agents just write code works.
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Frequently Asked Questions

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