- Specialization
- Computer Vision
- Business
- Software Applications
- Market
- Software Development
- Ecosystem
- Agents
- Customers
Developersand 1 more customers valuesLess
- Enterprises
More details
- Organization type
- Commercial Company
- Ecosystem
- Agents
- Business
- Software Applications
- Specialization
- Computer Vision
- Market
- Software Development
- Customers
- Developers, Enterprises
- Delivery models
- Hosted Software
- Revenue models
- Not classified
- Listing status
- Not classified
- Latest equity round
- Pre Seed
- Reported funding amount
- Not classified
About
Docket builds AI-driven, end-to-end QA testing software for software teams and enterprise engineering organizations. Its platform lets users describe tests in plain English and uses vision-based, human-like interactions with self-healing automation across web, mobile, and desktop applications, reducing flaky selectors and maintenance.
Market
Docket competes in AI-powered QA and end-to-end test automation, targeting teams that need broader regression and acceptance-test coverage without maintaining brittle selector-based scripts. Its differentiation is a vision-first approach that interacts with applications through screen coordinates like a human, combines natural-language authoring with AI for unpredictable flows, and self-heals when interfaces change across web, mobile, and desktop applications.
Docket targets QA, software assurance, and software development teams at startups through enterprises, especially organizations operating complex web and native mobile applications with frequent releases. Primary buyers and users include QA leaders, software assurance directors, software development leaders, and nontechnical stakeholders who need to create or understand acceptance and regression tests.
At a Glance
Problem
Docket addresses the maintenance burden and brittleness of conventional end-to-end QA automation. Selector-based tests break when buttons move, page structures change, or applications evolve, forcing engineering and QA teams to spend hours repairing scripts instead of expanding coverage. The economics are particularly painful for teams shipping frequent releases: Docket’s customer materials cite 60% faster test validation, 85% faster releases, and 80% faster test creation.
The primary use case is regression and acceptance-criteria validation for complex, frequently changing applications. Teams can validate Jira requirements through the user interface and run complete regression suites before releases, including across multiple modules, integrations, and long clinical or business workflows.
Product / Service
Docket is a vision-first, AI-driven QA testing platform for web, iOS, Android, and desktop applications. Users write end-to-end tests in plain English, while multimodal agents interact with applications as people do rather than relying on brittle DOM selectors. The platform can work from real user-session data, use screen coordinates, and adapt when interface elements move or selectors change.
The delivery model is a software platform offered through demos and a free-start or implementation-led launch motion. Its benefit is lower test-authoring and maintenance effort: deterministic flows handle standard interactions, self-healing corrects minor UI drift, and AI steps handle dynamic or unpredictable states. This is intended to provide broader, more durable coverage without constant script rewrites or human intervention.
Market
Docket competes in AI-powered test automation and end-to-end software quality assurance, with a differentiated vision-based approach spanning multiple application surfaces. Its own competitive materials position it against TestSigma, Tricentis, Mabl, and Stably AI, contrasting Docket’s autonomous, vision-first agents with tools that primarily depend on DOM selectors, object properties, or generated Playwright code.
The company appears to have early commercial traction rather than being only a pre-revenue concept. Its website features customer spotlights for eXp Realty, Centerpoint Connect, and Nest Genomics, with quantified claims around faster validation, releases, and test creation. Y Combinator lists Docket as founded in 2025 and records a launch offer of free complete test-suite implementations for its next 10 customers; the public materials reviewed do not disclose a reliable revenue figure or customer-count scale.
Founders & Leadership
Funding History
Y Combinator
Recent News
A StartupIntros profile describes Docket as developing AI agents for automated web testing and quality assurance, focused on simulating user interactions for web development and QA teams.
The article highlights Docket’s autonomous agents for testing e-commerce checkout flows in plain English, including self-healing behavior when application code changes.
TestingTools.ai reviewed Docket as an AI-powered test automation platform that helps teams write and maintain end-to-end tests at scale, with an agent that writes and runs test cases.
Active Roles
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Get notified when they postBusiness Model
Docket appears to monetize as a paid, sales-assisted B2B enterprise software platform: prospective customers book demos, while third-party coverage identifies the product as paid with no free trial. Detailed public pricing was not disclosed; the company has used free test-suite implementations as a launch promotion.