Delivery Stories

Results, Not Just Test Reports

How we build Automation frameworks and AI quality practices that teams keep using after we hand them over.

Playwright Frameworks AI Evaluation Harnesses Honest Metrics Only

Test Automation · SaaS

Playwright Framework Built From Zero to CI-Gated Releases

The Challenge

A product team relied on slow Manual regression before every release. Coverage was undocumented, releases were delayed, and confidence in 'green' was low.

What We Built

A Playwright + TypeScript framework designed for maintainability, page-object architecture, stable data-testid selector strategy, parallel execution, and CI integration so every merge runs the suite automatically.

Senior Automation lead + Automation engineer Framework foundation delivered in the first weeks, coverage grown sprint by sprint

The Results

· Regression that took days of Manual effort now runs automatically on every merge
· Flake-resistant selector strategy keeps green builds trustworthy
· Framework handed over with documentation, the client's team can extend it themselves

AI Quality Engineering · GenAI Product

QEAP: A Quality Engineering Practice for AI Features

The Challenge

AI features fail differently, hallucinations, prompt regressions, and RAG retrieval errors that no traditional test suite flags. The team had no way to measure whether an LLM change made things better or worse.

What We Built

An AI quality engineering delivery (QEAP) covering LLM output evaluation, prompt regression Testing, RAG retrieval accuracy checks, and hallucination detection, with a repeatable evaluation harness the team runs on every model or prompt change.

AI QA practice lead + QA engineer Evaluation harness stood up first, then expanded across features

The Results

· LLM changes are now measured, not eyeballed, every prompt change runs the evaluation suite
· RAG retrieval accuracy is tracked against a golden dataset
· Hallucination and bias checks run before AI features reach users

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