Goodbye Flaky Tests — Hello Docket
For most modern software teams, end-to-end (E2E) testing is a paradox. It’s critical for delivering reliable web applications, yet implementing and maintaining it often feels like a burden. The core issue? Traditional testing tools like Cypress and Playwright require detailed scripting, brittle selectors, and tedious upkeep. Even no-code testing platforms aren't immune — they still rely on error-prone CSS selectors and manual flow mapping.
These challenges produce a familiar tradeoff: either move fast and risk bugs slipping into production, or slow development to ensure test coverage. Many teams find themselves doing just enough testing to pass basic QA, knowing full well their test suite could break with the next UI update. Maintenance debt grows, confidence erodes, and manual testing creeps back into daily workflows.
Docket was born out of this frustration. The startup’s co-founders, engineers themselves, knew firsthand how inefficient and frustrating testing could be — and decided to reimagine it with AI and automation at its core.
How Does Docket Transform the QA Process?
Docket takes a revolutionary approach to software testing by leveraging AI agents and real user session data. At its heart is a deceptively simple premise: you write tests in plain English, and Docket handles everything else — interpreting the instructions, interacting with the page like a real user, and keeping tests up to date.
Here’s what makes the Docket approach stand out:
- Plain English Instructions: Developers and QA teams can define test flows with natural language inputs like “Log in, search for a Y Combinator t-shirt, add two to cart.” No need to learn test scripting frameworks or map out user paths manually.
- Multimodal AI Agents: Instead of relying on brittle CSS selectors or DOM structures, Docket’s AI agents interact with web applications the way a human would — combining visual cues, layout context, and semantic data to complete actions.
- Automatic Test Updates: By analyzing real user session data, Docket ensures that tests evolve alongside the product. When a button moves or a flow changes slightly, Docket adapts, reducing breakage and virtually eliminating maintenance overhead.
In essence, Docket acts as an intelligent co-pilot for QA — understanding your web app, staying in sync with user behavior, and freeing your team from the tedious work of constant test upkeep.
Who Are the Founders Behind Docket?
Docket was co-founded by Nishant Hooda and Boris Skurikhin, two engineers who have lived through the daily grind of shipping software at scale. They met while working at AWS, where the seeds of Docket were planted through shared conversations about the challenges of testing.
Nishant previously worked at Stripe and Brex, where speed and scale were constant themes, but testing often slowed product velocity. Boris brought his experience as a Quantitative Software Engineer at Citadel and later contributed to growth engineering at Patreon. Together, their technical backgrounds span high-performance trading systems, financial infrastructure, and consumer tech — all domains where bugs can be costly and testing is mission-critical.
Both co-founders share a conviction: testing shouldn’t be a bottleneck. It should be as intuitive and effortless as writing a feature. This belief now drives every decision at Docket.
What Sets Docket Apart from Traditional and No-Code Testing Tools?
Many tools have tried to make QA easier. Some reduced code complexity; others introduced visual workflows. But most still fall short when it comes to maintenance and adaptability.
Here’s where Docket creates a clear break from the past:
| Feature | Traditional Tools | No-Code Platforms | Docket |
|---|---|---|---|
| Test authoring | Requires scripting (e.g., Cypress, Playwright) | Point-and-click or visual flow builders | Plain English commands |
| Test stability | Breaks with UI changes (selectors) | Often reliant on fragile elements | AI agents mimic real users |
| Maintenance | High — requires regular updates | Moderate — but still needs manual flow updates | Minimal — synced with real user data |
| Technical barrier | High — needs engineering resources | Moderate — low-code tools often require training | Low — accessible to non-engineers |
Docket’s true innovation lies in continuous adaptation. Its tests don’t go stale because they’re rooted in real-world behavior. And they don’t rely on static definitions — the AI evolves with the app.
Why Is Now the Right Time for a Solution Like Docket?
The rise of multimodal AI models, LLMs, and intelligent agents has created an inflection point for QA automation. Tasks that once required careful scripting or human oversight can now be handled with contextual understanding and adaptive reasoning — exactly the capabilities Docket harnesses.
At the same time, the pace of web development is faster than ever. Product teams deploy changes daily or even hourly. In this environment, testing systems must keep up or risk becoming irrelevant. Rigid automation pipelines no longer fit — the modern stack needs self-healing, flexible infrastructure that scales with agility.
Docket is perfectly positioned at this crossroads. By fusing AI with real user insights, it provides a smarter way to test that fits naturally into today’s workflows and tomorrow’s development paradigms.
How Does Docket Fit into a Modern Dev Team’s Workflow?
Docket is designed to integrate seamlessly with the tools developers already use. Its focus is not to replace human testers or QA engineers, but to empower them — reducing busywork, increasing coverage, and accelerating feedback loops.
Some key use cases include:
- Pre-release testing: Quickly validate critical user flows with minimal effort.
- Regression prevention: Ensure existing functionality stays intact as the app evolves.
- Bug reproduction: Recreate reported issues by referencing real session data and replaying them as automated tests.
- Team collaboration: Enable product managers, designers, and non-technical stakeholders to contribute to test coverage using natural language.
In every case, Docket removes friction. It’s not another system to manage — it’s a supportive layer that enhances team velocity and product quality.
What’s Next for Docket?
Currently active and based in San Francisco, Docket is part of the Spring 2025 YC batch and is already making waves within the engineering community. Early adopters are praising its usability, adaptability, and the sense of “magic” that comes from watching a test evolve without manual intervention.
The team is focused on scaling the platform, expanding integrations, and adding even more intelligence to their AI agents. As more companies adopt Docket, the product is also gaining valuable insights into common testing patterns — enabling smarter defaults and even predictive test generation in the future.
Beyond web testing, the team sees long-term potential in applying their approach to mobile apps, desktop interfaces, and even enterprise software stacks.
Why Should Engineering Leaders and QA Teams Care About Docket?
For engineering leaders, Docket offers a compelling ROI: better quality assurance with less developer time invested. It turns testing from a liability into an advantage — enabling teams to move fast and stay confident.
For QA teams, it’s a game-changer in daily workflow. No more updating brittle tests after every design change. No more sifting through broken selectors. Just clear, up-to-date validation based on how users actually use the product.
And for startups operating in lean mode, Docket is a powerful way to scale testing without scaling headcount.
Conclusion: Is Docket the Future of Web Testing?
If software is eating the world, then testing is its immune system — and Docket is giving it a long-overdue upgrade. With natural language test authoring, AI-powered execution, and session-based updates, it redefines what modern QA can look like.
As development cycles shrink and the cost of failure rises, tools like Docket aren’t just helpful — they’re necessary. Whether you’re a startup founder, a tech lead, or a QA specialist, Docket offers a smarter, simpler way to ensure software quality in a high-speed world.