TestSprite
To become the autonomous verification layer for the AI-native development era, eliminating manual testing bottlenecks through AI agents.
At a Glance
- AI-native startups
- Enterprise software development teams
- High-velocity engineering organizations
AI Tools by TestSprite
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TestSprite
AI Test Case Generation Platform
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Latest News
TestSprite Raises $6.7 Million Seed Round to Become the Testing Backbone of the AI-Native Development Era
Seattle startup TestSprite, which automates testing of APIs and web apps, raises $1.5M
TestSprite expands integration with major AI IDEs including Trae and Cursor
TestSprite selected for Techstars Seattle 2024 cohort
Products & Services
An agentic verification layer that automatically generates, executes, and heals tests for AI-native development.
Model-Centric Programming server for intent parsing and codebase inference, allowing tests to be generated directly from requirements.
A free-to-use version of the testing platform for developers and small teams.
Market Position
Positions itself as an 'agentic' testing platform that acts like a virtual QA engineer, differing from traditional script-based automation by autonomously understanding intent and maintaining tests.
Leadership
Founders
Yunhao Jiao
CEO. Former Software Development Engineer at Amazon Business and AWS. Research Assistant at Zhejiang University and Visiting Researcher at University of Michigan. Alumnus of Yale University.
Rui Li
CTO. Co-founder and lead technologist. Background in engineering at major tech companies (Amazon/Google/Microsoft cited for founding team).
Shawnie Shan
Co-founder. Yale University graduate. Involved in the early development and growth of the platform.
Executive Team
Yunhao Jiao
CEO & Co-founder
Former Amazon engineer and NLP researcher.
Rui Li
CTO & Co-founder
Technical lead for the autonomous testing platform.
Board of Directors
Founding Story
Founded by a team of former Amazon, Google, and Microsoft engineers who experienced first-hand the inefficiencies of manual testing in fast-paced development environments. The company aims to solve the 'AI quality crisis' where code generation speed exceeds testing capacity.
Business Model
Revenue Model
SaaS subscription and potentially API usage-based fees for enterprise and high-volume testing.
Pricing Tiers
Open-access for individual developers and small projects.
Full-stack autonomous testing with continuous regression guardrails and dedicated support.
Target Markets
- AI-native startups
- Enterprise software development teams
- High-velocity engineering organizations
- Automating QA for AI-generated code
- Continuous regression testing for high-velocity teams
- Full-stack end-to-end testing (API, UI, Data)
- Speeding up CI/CD pipelines
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