We design and build AI-native test automation, including touchless test creation, self-healing tests, and parallel execution, then hand it to your team trained and ready. Playwright and TypeScript by default, or the stack you already run. For larger programs, we own quality end to end, all the way to the go-live decision.
Recommended, with one item monitored through hypercare.
Your stack, not ours. Playwright and TypeScript are our default for a new build, but we work in the frameworks your team already runs, including Cypress, WebdriverIO, Karate, and REST Assured, and with comparable cloud, CI, and gateway tools such as MuleSoft, Google Cloud, or Azure DevOps.
From the founder’s prior work leading quality and release:
Modern front ends change constantly, so conventional automation breaks and teams quietly fall back to manual testing.
Faster sprints, framework upgrades, and vendor platform releases change behavior faster than regression suites can keep up.
Go/no-go calls get made on status meetings instead of traceable results your stakeholders can review.
A suite can be entirely green while the rate is miscalculated, the application routes to the wrong queue, or the record downstream never updates. Passing is not the same as correct, which is why we verify the business result, not just the screen.
The easiest way to start is a Health Check. From there, we build your automation or, for larger programs, own quality end to end.
Start here
Two weeks, fixed price agreed before we start
An AI-assisted review of your application’s risk, test coverage, automation health, and release readiness, with a prioritized plan you keep either way.
What you getOur core offer
Fixed scope · framework in 1–2 weeks, then coverage every sprint
We build AI-driven automation for your critical journeys in a framework that fits your stack, wire it into your pipeline, and train your team to own it. When we leave, everything stays with you.
Scoped to your program
Quality owned from kickoff to closure for a launch, migration, or major rollout, plus Release Assurance for every release after.
3-week AI Evaluation Sprint · fixed price
Chatbots, RAG assistants, and agents get calibrated LLM-as-judge evaluation that gates every release, right in your pipeline.
Most firms use AI to write test code faster. We build it into the whole delivery system, in four repeating stages: create the tests, run them on every change, maintain them so they stay trustworthy, and diagnose what they find. Each stage feeds the next, which is what keeps a suite alive instead of slowly decaying.
Requirements and user stories go in; traceable test cases and runnable scripts come out, with no manual scripting and no duplicates.
When a screen changes, on web or mobile, locators heal and are validated before they’re accepted. Assertions are never healed, so a real failure can never be quietly turned into a pass.
Playwright sharding across containerized CI pods turns multi-hour regression runs into a fraction of the time, so a full suite fits inside your release window.
When a test fails, a defect is filed automatically in Jira with screenshots, the Playwright trace, and a network analysis from the HAR file that pinpoints the failed or slow API call.
A review agent checks every test change against your standards before merge, and a triage agent classifies each failure as a real regression, an application change, or noise.
Every UI run captures the API calls behind each screen. We turn that real traffic into fast API-level regression tests, so integrations are covered without writing each test from scratch.
Real failures hide in two places: deep inside one system, and in the handoffs between systems. We design end-to-end and regression testing for both.
Each application is tested top to bottom: the screens users see, the APIs and services behind them, and the data they write, so a passing UI never hides a broken record.
Business flows are tested the way customers experience them, across applications, integrations, and API gateways, like an application submitted online that has to land correctly in Salesforce and downstream systems.
Tiered suites, from smoke to targeted to full regression, with risk-based selection that runs the right tests for each change instead of everything, every time.
One source of truth for test cases, organized by feature and by business flow, traceable to requirements, versioned, deduplicated, and retired when they no longer earn their place.
One quality practice, applied to any web application, with specialist practices for the platforms where generic testing falls short.
Customer-facing and internal web apps, mobile web and native apps, the APIs behind them, and the integrations between them, with automation architected to scale across a whole portfolio.
We test through the screen and then verify what actually landed in the data, across Sales Cloud, Service Cloud, Financial Services Cloud, and nCino.
A page can look perfect on author and still break on publish. We test authoring, publishing, and the handoff between them.
Chatbots, generative features, and agents tested with LLM-as-judge evaluation built into your regression suite.
Every engagement is scoped, built, and handed over by a senior quality architect. Nothing gets passed to a junior bench after the sale.
Your tests are open-source Playwright code in your own repository. No platform license, no lock-in, and nothing to lose if we part ways.
Whether we build your automation or run your launch, you get traceable results and a clear go/no-go recommendation your stakeholders can review.
We own quality across the whole program instead of joining for a testing phase at the end, with AI assisting every phase.
Scope, success criteria, and a map of where your application carries risk.
Traceable test strategy, environments, and test data.
The quality workstream run like a project, across every team involved.
A Playwright regression suite for the journeys that matter most.
Deployment validation, cutover, and a go/no-go backed by evidence.
Stabilization, defect burn-down, and a suite your team keeps running.
QualityFirstQA was founded by Avneet Dhanoa after 15 years of taking banking, semiconductor, technology, and enterprise SaaS programs from build to go-live. The person who scopes your engagement is the person who builds it.
Three programs from the founder’s prior work, anonymized. They are why the method on this site looks the way it does.
Banking
A new digital bank had a fixed launch date and no independent quality function. A 20-person QA and release organization was built around it, with a traceable test strategy and an evidence-based go/no-go. It launched on schedule.
Semiconductor
A large enterprise portfolio had no common automation approach. A Playwright and TypeScript framework was built from an empty repository, with standards and CI guardrails, and adopted by a 10-engineer team across more than 100 applications.
Technology
A global line of business releases with test teams in five regions and a fixed CI window. AI-augmented tooling, self-healing tests, and sharded execution keep full regression inside that window, and every release ends in a documented go/no-go.
Tell us where your testing stands and what you want to change. We’ll suggest whether a Health Check, an automation build, or a bigger engagement fits best.