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QA & Automation
Services

QA Engineering & Test Automation

A unified quality engineering practice — from strategy and manual testing through to fully automated pipelines — built to scale alongside your product.

  • Shift Left approach for early defect detection
  • End-to-end coverage from strategy to automation
  • Agile-native frameworks that scale with your team
  • Transparent reporting at every stage

What We Deliver

Four tightly integrated service areas — including specialised testing for AI-powered applications — covering the full quality engineering lifecycle.

01

AI Application Testing

AI-powered applications behave non-deterministically — outputs vary, context matters, and failures surface in ways traditional testing cannot catch. We apply specialised QA practices across three AI application tiers, evaluating output quality, retrieval accuracy, pipeline reliability, and end-user experience to help you ship AI features with confidence.

Vanilla AI

Direct LLM API calls

  • Output quality, relevance & factual accuracy testing
  • Hallucination detection & response consistency testing
  • Prompt regression testing (prompt change impact analysis)
  • Multi-turn conversation flow & context retention testing
  • Response latency & LLM API performance testing
  • Error handling, rate-limit & graceful degradation testing
  • Token boundary & truncation edge case testing
Static RAG

LLM + fixed knowledge base / vector store

  • All Vanilla AI checks
  • Retrieval accuracy & top-k relevance validation
  • Context faithfulness testing (response grounded in retrieved docs)
  • Knowledge base coverage & gap analysis
  • Embedding quality & similarity threshold validation
  • Source attribution accuracy & citation testing
  • Out-of-scope query handling & fallback behaviour
Dynamic RAG

Real-time retrieval, live data sources

  • All Static RAG checks
  • Real-time data freshness & stale content handling
  • Retrieval pipeline latency & performance under load
  • Data source failover & resilience testing
  • Concurrent retrieval & multi-user load testing
  • Pipeline regression testing across data source schema changes
  • End-to-end integration testing across live data feeds
LLM Output TestingHallucination DetectionRAG Quality TestingPrompt RegressionPipeline Testing
02

QA Strategy, Design & Process Optimization

We craft tailored QA strategies aligned to your product lifecycle and business goals — defining test scope, designing test cases, and embedding quality from the very first sprint using a Shift Left approach. We also audit your existing testing workflows, identify inefficiencies and bottlenecks, and implement structured defect prevention strategies. Our metrics-driven process improvements reduce escape defects and increase your overall release confidence.

Shift Left TestingTest Case DesignFunctional TestingRisk-Based QADefect PreventionProcess AuditRoot Cause AnalysisQuality Metrics
03

Performance and NFR Testing

Beyond functional correctness, we validate that your application performs reliably under real-world conditions. Load and stress tests are designed and executed using Apache JMeter, k6, and Gatling — simulating thousands of concurrent users to surface bottlenecks before they reach production. Lighthouse and WebPageTest drive web performance audits, while BrowserStack and Sauce Labs cover cross-browser and cross-device compatibility at scale.

Apache JMeterk6GatlingLighthouse / WebPageTestBrowserStack / Sauce Labs
04

End-to-End Automation & CI/CD Delivery

We define ROI-driven automation strategies — knowing what to automate is as critical as how. We build custom, scalable, and maintainable frameworks using Page Object Model and Data-Driven design, then deliver comprehensive functional automation across web, mobile, and API surfaces. Test suites are integrated directly into your CI/CD pipelines via GitHub Actions, Jenkins, or Azure DevOps — with real-time dashboards, automated alerts, and trend reporting on every build.

SeleniumPlaywrightCypressAppiumWeb / Mobile / APIGitHub ActionsJenkinsAzure DevOpsQuality Dashboards

Our QA Toolkit

Open-source tools we use every day — proven, community-backed, and built for scale.

Web Automation
SeleniumPlaywrightCypressTestCafe
Mobile Automation
AppiumDetoxEspressoXCTest
API Automation
PostmanRestAssuredKarate DSLSoapUI
Performance Testing
Apache JMeterk6GatlingLocust

One team. End-to-end quality.

From your first sprint to production at scale — we're your quality partner.

Discuss Your QA Needs

Frequently Asked Questions

How we engage, what we automate, and where to start.

We evaluate AI applications across three tiers: Vanilla AI (direct LLM API calls), Static RAG (fixed knowledge base / vector store), and Dynamic RAG (real-time retrieval). Testing covers output quality, hallucination detection, RAG pipeline accuracy, prompt regression, and agentic workflow reliability.

Our engineers work as an embedded extension of your team rather than an external audit function. We join your sprint cadence from day one, applying a Shift Left approach so defects are caught during development rather than at release. You get one accountable team covering strategy, manual execution, automation, and performance — no handoffs between vendors. Delivery is visible throughout: real-time quality dashboards, automated alerts, and trend reporting on every build. Engagements scale up or down with your release velocity.

Automating everything is rarely the right answer for a startup. Our first question is which layer to automate at, not which tool to use: where the UI is still changing rapidly, or the same journey exists across both mobile and web, we automate at the API layer — coverage holds while the interface moves, and a single suite serves every front end. From there we prioritise the tests that run most often and the critical customer scenarios your business depends on. Frameworks use Page Object Model and Data-Driven design so suites stay maintainable as your product changes, and they're wired into your CI/CD pipeline from the first test rather than bolted on later.

Before your first traffic spike, not after it. Most startups discover their limits during a launch, a campaign, or a seasonal peak — when the cost of failure is highest. We design load, stress, soak, and spike tests using Apache JMeter, k6, and Gatling, simulating thousands of concurrent users to surface bottlenecks while there's still time to fix them. Web performance is audited with Lighthouse and WebPageTest, and cross-browser and cross-device behaviour is validated at scale through BrowserStack and Sauce Labs. The output is a clear picture of where your application breaks and what to change first.

Either way we start with an assessment, not a rebuild. For teams starting from zero, we define test scope, design the test cases, and set up the process and tooling aligned to your sprint workflow — a QA practice that fits your stage rather than an enterprise one you'll outgrow. For teams with existing tests, we audit current workflows first, identify inefficiencies and bottlenecks, and keep what works. From there we apply structured defect prevention and metrics-driven improvements that measurably reduce escape defects and raise release confidence.

Ready to Elevate Your Product Quality?

Let's build a quality engineering practice that gives your team confidence to ship fast — without breaking things.

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