Will you recommend replacing our current tooling?

Only where the evidence supports it, and we will show you the comparison in writing first. A stable suite maintained by people who understand it is usually worth more than a modern one nobody owns. Migration is a real recommendation when maintenance costs exceed the value returned, not a default position.

Who owns the code and the licences?

You do. Frameworks, harnesses and scripts live in your repository under your licence from the first commit. Everything we build is open-source tooling by default, so there is no vendor lock-in and no per-seat platform fee attached to your Testing.

Can your Engineers work inside our existing framework?

Yes, and that is the most common arrangement. We work in your language, your repository and your conventions. If your framework has architectural problems we will name them and propose incremental fixes rather than starting again by default.

Our Stack

Our Technology Stack,Backed by Real Delivery

Q: Do you only work with the technologies listed here?

No. These are the eight where our depth is greatest and where we will make architectural recommendations. We work daily with Docker, Kubernetes, Jenkins, GitHub Actions, GitLab CI, Grafana, PostgreSQL, MongoDB, Azure and Google Cloud, and we adapt to a client's existing stack rather than asking them to adopt ours.

Eight core technologies, each with real delivery experience behind it, from Playwright frameworks to LLM evaluation harnesses and performance tooling.

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Playwright & Selenium AWS & Python OpenAI · Claude · Gemini

Playwright

Our primary Automation stack for modern web apps

Playwright with TypeScript is our default stack for new Automation builds: auto-waiting, parallel workers, trace-viewer debugging, and one API across Chromium, Firefox, and WebKit. We design frameworks around it that survive handover, page objects, selector contracts, data isolation, and CI gates.

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Selenium

Deep experience across grids, languages, and legacy estates

Founder-led by 16+ years of enterprise QA leadership, we bring deep Selenium know-how: large grids, Java/Python/C# bindings, and the realities of maintaining suites across thousands of tests. If your Selenium investment is sound, we make it stable and fast, no rewrites for the sake of fashion.

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Postman & Karate

API functional, contract, and Automation tooling

APIs are where integration bugs live. We use Postman for exploratory and collection-based coverage and Karate for BDD-style automated API suites, validating REST, GraphQL, and SOAP services on contracts, auth, error handling, and edge cases before any frontend depends on them.

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AWS

Cloud environments, pipelines, and test infrastructure

We test applications where they actually run: AWS. That means environment-parity validation across dev/stage/prod, failover and scaling behavior checks, CI/CD quality gates in CodePipeline or GitHub Actions, and test infrastructure (parallel runners, ephemeral environments) that keeps suites fast.

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OpenAI · Claude · Gemini

LLM integration engineering, validated by our own AI QA practice

We build LLM-powered features, chatbots, agents, RAG knowledge systems, on OpenAI, Claude, and Gemini, and validate them with our own AI Quality Engineering practice: prompt regression suites, retrieval accuracy against golden datasets, and hallucination checks before anything reaches users.

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Python

Automation, tooling, and AI evaluation harnesses

Python powers much of our tooling: pytest-based Automation, API test suites, data validation utilities, and the AI evaluation harnesses behind our LLM Testing practice. When your stack is Python, our Engineers speak it natively, from fixtures to CI plumbing.

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Cypress

Component and E2E Testing for JavaScript-first teams

Cypress earned its place with fast feedback and a debugging experience frontend developers actually enjoy. We build and stabilize Cypress suites, E2E and component tests, network stubbing, CI parallelization, and give honest guidance on when Cypress is the right call versus when Playwright's multi-browser and multi-tab model fits better.

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JMeter & k6

Load, stress, and spike Testing with numbers you can act on

Every system has a breaking point; JMeter and k6 are how we find yours before your users do. We model workloads from real production traffic, run load, stress, spike, and soak tests, and translate the graphs into named bottlenecks, slow queries, saturated pools, API latency, with fixes attached.

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Also in Our Toolbox

The Wider Toolkit Behind Every Engagement

Cypress, Jenkins, Docker, Kubernetes, TypeScript, Java, Node.js, k6, GitHub Actions, GitLab, Grafana, PostgreSQL, MongoDB, Azure, and Google Cloud, alongside our core Playwright, Selenium, Postman, Python, and AWS stack.

Cypress

Jenkins

Docker

Kubernetes

TypeScript

Java

Node.js

GitHub Actions

GitLab CI

Grafana

PostgreSQL

MongoDB

Azure

Google Cloud

We pick tools for the team that inherits them

A technology page is usually a list of logos.

Selenium

Cypress

Automation in Python

A technology page is usually a list of logos. This one is a set of positions, because tool choice is the decision that most often decides whether a Testing investment survives its second year. The question we ask on every engagement is not which tool is best in the abstract, it is which tool the people maintaining this in twelve months will still be able to read, extend and trust.

That leads to unfashionable answers sometimes. We keep healthy Selenium estates running instead of proposing rewrites. We tell Cypress teams to stay on Cypress when their product is single-origin and their Engineers are JavaScript-first. We build Automation in Python when a client's platform team is Python-first, even though our own default is TypeScript. A framework nobody on your team can maintain is a liability no matter how modern it is.

The core eight, and what each is for

Playwright: our default for new browser Automation: auto-waiting, parallel workers, trace-based debugging, one API across Chromium, Firefox and WebKit.

Selenium: deep experience across Java, Python and C# bindings, large grids and legacy estates, with honest migrate-versus-maintain advice.

Cypress: supported and stabilised for JavaScript-first teams, with genuinely strong component Testing for design systems.

Postman & Karate: functional, negative and contract coverage for REST, GraphQL and SOAP, running in your CI rather than a personal workspace.

JMeter & k6: load, stress, spike and soak Testing that ends in a named bottleneck rather than a graph.

Python: pytest Automation, data reconciliation harnesses, synthetic data generation and AI evaluation runners.

AWS: test environments as code, containerised pipeline execution, and resilience Testing of the platform itself.

OpenAI · Claude · Gemini: LLM integration engineering shipped with a golden dataset and an evaluation harness attached.

Tooling is downstream of strategy

None of this matters without a coverage map.

Quality Engineering practice

AI Quality Engineering

None of this matters without a coverage map. Choosing Playwright before deciding which journeys carry real risk produces a fast suite covering the wrong things. So engagements start with strategy, the disciplines in our Quality Engineering practice, ranked by what would actually hurt if it broke, and the stack follows from that. Where a product ships AI features, AI Quality Engineering adds an evaluation layer conventional tooling does not provide.

Beyond the core eight we work daily with Docker, Kubernetes, Jenkins, GitHub Actions, GitLab CI, Grafana, PostgreSQL, MongoDB, Azure and Google Cloud. If your stack is not listed, it is almost certainly one we can work in, the practice is portable even where the tool names are not.