About
A QA company built by an engineer who does the work
Afoxlabs exists for a pattern seen many times from the inside: teams that ship frequently, want their testing to keep pace, and need QA work delivered on a schedule — without first building a full QA organisation.
Why Afoxlabs
Seven-plus years as an SDET across enterprise software products — manual testing, UI and API automation, framework design, performance and chaos testing, CI/CD ownership and release leadership — makes one pattern easy to see. Strong engineering teams often find that their testing has simply not had the time to grow at the same rate as their product.
Building an in-house QA team is a great long-term answer, and it takes time to recruit and onboard. Bringing in extra people works well when you want to direct them day to day. Many teams need something more immediate: a specific piece of QA work, done well, by a date they can plan a release around.
That is what Afoxlabs does. You hand us defined QA work — a feature to test, a regression pack to automate, a framework to build, a release to validate — and we deliver it on a committed date for a fixed quote. Everything is built inside your repositories, so it keeps running long after the delivery.
Built from hands-on delivery experience
When you choose a QA partner, it helps to know that the person scoping your work has built and maintained the same thing themselves. Every Afoxlabs engagement is scoped and led by an engineer who has designed automation frameworks from an empty repository, maintained them through years of product change, and been accountable for whether a release shipped.
That includes the parts that are easy to leave off a services page: owning QA environments, managing Jenkins jobs, leading twenty-plus continuous delivery releases, driving customer-reported defects through investigation to resolution, and mentoring other engineers on framework design. That wider experience is what keeps a test suite valuable for years after it is written.
On the AI work
The AI quality page is grounded in hands-on work. It comes from having built AI-assisted automation solutions on LLM technologies in a production engineering context, and from generating internal tooling with AI assistance — including a complete Python framework.
Doing that work is also where the caution comes from. Models are genuinely useful for drafting tests, generating data and clustering failures, and genuinely unreliable as the thing that decides whether a result can be trusted. Hence the rule we will not bend: AI proposes, controlled automation and human review decide.
How the company is set up
Focused and senior. Every engagement is led by someone who has built and maintained large automation suites, and we only commit to delivery dates we have the capacity to meet — so a date from Afoxlabs is one you can plan around.
Our core is web and API testing, test automation and CI/CD, extending into performance, resilience and AI evaluation whenever a client's product needs it.
The long-term direction
Recurring problems across clients become reusable internal tooling. Some of that tooling becomes a public Labs tool. If one of those ever earns real, demonstrated demand, it may become a product. That sequence matters: tools follow from client work rather than being invented in the hope that someone wants them, and the services business stays financially ahead of the experiments throughout.
Depth behind the services
What "hands-on" actually means here
Every service on this site maps to work done repeatedly in a production engineering context.
- Automation frameworks
- Designed and implemented frameworks from scratch in Java + Playwright + TestNG, Java + Selenium + Cucumber, and Python. Framework architecture, not just test writing.
- API testing & automation
- REST Assured, Jersey client, Postman and Insomnia. Feature-level API automation, regression suites, and owning features end to end through to release.
- UI automation
- Playwright and Selenium, including Selenium Hub for distributed execution. Building suites, and the less glamorous work of keeping large ones stable.
- CI/CD & release ownership
- Jenkins job ownership for UI and API automation, DevOps release cycles, and leading 20+ continuous delivery releases across multiple environments.
- Performance & resilience
- JMeter load testing, plus chaos engineering with Gremlin — fault injection measured through JMeter and Grafana to find what actually breaks under failure.
- AI-assisted quality engineering
- Production AI-assisted automation built on LLM technologies, and several internal tools generated and maintained with AI assistance under human review.
- Environments & infrastructure
- Owned and maintained QA environments for testing and release validation, including customer-like environments. Kubernetes and Docker at a working level.
- Beyond the keyboard
- Test strategy authoring and review, release planning, quality reviews, production validation, customer-reported issue ownership, published technical white papers, and mentoring engineers on framework design.
The path here
How the experience accumulated
Enterprise software products, from maintaining someone else's automation to owning release quality and designing the frameworks. Employer names are left out deliberately — they are available on request under NDA, and named clients are never implied by a former employer's logo.
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Enterprise software. Fixing UI and API automation, reporting on CI/CD cycles, and learning where automation breaks by maintaining someone else’s suite — which is the fastest education available in why frameworks matter.
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Owning features through complete testing. Building UI automation in Java, Selenium and Cucumber, and API automation in Java and Jersey. Running performance tests in JMeter, managing Jenkins jobs, authoring test strategy, and publishing technical white papers.
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Chaos testing with Gremlin, JMeter and Grafana. Leading 20+ continuous delivery releases across multiple environments. Building internal tools to cut manual effort, and owning customer-like test environments.
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Designing automation frameworks in Java, Playwright and TestNG. Building AI-assisted automation on LLM technologies. Owning customer-reported issues end to end, participating in release planning and production validation, and mentoring engineers on automation practice.
What we believe
Six opinions that shape every engagement
Stated plainly so you can disagree with them before we work together rather than afterwards.
Quality is a system, not a person
Great teams work hard on quality. It scales best when it rests on process, automation and gates rather than on individual memory and availability.
The API layer is underrated
API tests are fast, stable, inexpensive to maintain and catch most logic regressions early. Start there, and the UI suite can focus on what is genuinely UI.
A trusted suite is the goal
A test suite earns its value when the whole team believes its result. Flakiness is a defect class, and it deserves the same care as any other.
Coverage numbers are not the goal
Eighty percent coverage of trivial paths is worth less than complete coverage of the five journeys that generate your revenue. Risk decides priority, not convenience.
AI belongs in the workflow, not in the verdict
Models are genuinely good at drafting, generating data and clustering failures. They should never be the thing that decides a release is safe.
Everything we build is yours
All work lives in your repositories from day one, so your team can run, extend and own it with or without us.
Have a QA task? Get a committed timeline.
Describe the work — a feature to test, a regression pack to automate, a release to validate. Within two business days you get a written scope, a delivery date in days, weeks or sprints, and a fixed quote.