Checksum
Checksum is a continuous quality platform that automatically generates, runs, and maintains end-to-end and API tests for engineering teams shipping AI-generated code.
At a Glance
- Enterprise Software
- AI-native Startups
- High-growth Engineering Teams
- QA Teams
AI Tools by Checksum
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Checksum
AI Playwright Test Automation
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Latest News
Checksum launches the Continuous Quality Loop for AI-written code
Checksum releases State of AI Code 2026 report
Checksum Launches the API Agent for advanced API testing
Checksum launches Continuous Quality Agent for AI code verification
Products & Services
A platform that uses AI agents to generate, run, and maintain E2E and API tests as Playwright code.
An agent that discovers endpoint relationships and chains calls to verify state and downstream effects.
An agent that runs on every PR, generating and executing tests specific to the code changes.
Market Position
Positions as the 'Verification Layer for AI-generated code,' offering 'Results-as-a-Service' where tests are delivered as standard Playwright code that the customer owns. Competes with legacy automation tools by eliminating maintenance overhead via autonomous AI agents.
Leadership
Founders
Gal Vered
Co-founder & CEO. Previously Product Leader at Google, CTO at SEER (Y Combinator), and a Navy officer. MBA from Northwestern Kellogg.
super{set}
A venture studio co-founded by Tom Chavez and Vivek Vaidya that partners with founders to build data and AI companies.
Executive Team
Gal Vered
Co-founder & CEO
Former Google Product Leader, CTO at SEER (YC), and Navy Officer.
Abigail Aragon
Head of Revenue
Founding AE at Checksum, previously building GTM motions at Empromptu.
Board of Directors
Founding Story
Gal Vered left Google in 2022 to address the 'software verification problem' he observed as a product leader. He partnered with the super{set} venture studio to build an AI-native quality platform that keeps pace with the speed of AI-generated code, moving from manual test maintenance to autonomous continuous quality.
Business Model
Revenue Model
Subscription-based 'Results-as-a-Service' model, pricing based on the number of workflows maintained.
Pricing Tiers
For teams building their first suite; 50 E2E workflows maintained.
For growing teams; 200 E2E workflows maintained, parallel execution.
For full coverage; 400+ workflows, includes API testing agent and SLA.
Target Markets
- Enterprise Software
- AI-native Startups
- High-growth Engineering Teams
- QA Teams
- Automating regression testing for fast-shipping teams
- Verifying AI-generated code at scale
- Reducing manual QA and test maintenance overhead
- Continuous API and E2E coverage
- Counterpart
- Ketch
- Postilize
- Söderberg & Partners