A quality company built by someone who did the work

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 impossible to miss. The teams that struggle with quality are rarely careless. They are usually competent teams whose testing has not scaled at the same rate as their product.

Their options are all bad. Hire a QA team: six months of recruiting, real management overhead, and a permanent cost structure for a capability they need built once and then maintained. Hire contractors: tester-hours with nobody accountable for the outcome. Ask developers to absorb it: uneven coverage and slower feature work, with quality still owned by nobody in particular.

Afoxlabs is the fourth option. We take responsibility for a defined quality outcome — coverage, automation, gates, reporting — and we build it inside your repositories so you are never dependent on us to keep it running.

Built from delivery experience, not a sales deck

The distinction that matters when you are choosing a QA partner is whether the person scoping your engagement has personally built and maintained the thing they are selling. Plenty of agencies put a commercial layer in front of junior testers. The work here is scoped and led by someone 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. Delivering testing is a small part of running quality. The rest is what separates a suite that survives from one that gets abandoned.

On the AI work

The AI quality page is not speculative positioning. 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

Deliberately small, and deliberately senior. Engagements are scoped so that the person doing the technical work is someone who has built and maintained large automation suites, not a junior executing a script written by someone else. That constrains how many clients we can take at once, which we consider a feature.

We start narrow — web and API testing, automation, CI/CD — and expand into performance, resilience and AI evaluation when a client's product genuinely needs it. A service catalogue that lists everything is a signal that nothing in it is a specialism.

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.