AI Test Data Generator
Local-first synthetic test-data toolkit that helps QA teams generate realistic, schema-valid records from JSON Schema, OpenAPI, and form contracts, with optional local AI assistance and no cloud keys required.
I help teams move from ad-hoc testing to maintainable quality engineering: custom frameworks, internal tools, reliable CI gates, and testing strategies shaped around the real product, users, and business risk.
The outcome is testing infrastructure your team can own: documented, maintainable, integrated into CI, and tailored to your stack, with AI/LLM validation added where it creates real value.
Custom test automation frameworks for web, mobile, API, and desktop products, built with maintainable architecture, reporting, CI integration, and clear team handoff.
AI-assisted QA automation workflows for test generation, test-data support, flaky-test triage, suite maintenance, and smarter validation around repetitive QA work.
AI/LLM evaluation pipelines for product teams, covering hallucination checks, RAG faithfulness, context relevance, semantic similarity, regression evaluation, and safety-focused validation.
API test automation for REST and GraphQL services, covering contracts, integrations, regression checks, smoke coverage, schema validation, and fast developer feedback.
Desktop and enterprise app test automation for Windows and Electron products, with resilient UI flows, environment setup, reporting, and visual validation where needed.
QA automation strategy, framework reviews, test architecture audits, CI quality gates, and practical guidance for engineering teams improving release confidence.
Open-source frameworks and tools — each opens its GitHub repository. A few more are on the way.
Local-first synthetic test-data toolkit that helps QA teams generate realistic, schema-valid records from JSON Schema, OpenAPI, and form contracts, with optional local AI assistance and no cloud keys required.
Cross-platform E2E orchestrator for running API, web, and mobile automation as one end-to-end journey, with shared scenario state, CLI execution, and unified reporting.
Production-ready Playwright + Pytest framework for maintainable web automation, with cross-browser runs, Allure reporting, reusable helpers, and clear example flows.
Python + Appium framework designed to run one maintainable suite across Android, iOS, hybrid apps, and mobile web, with reusable test structure.
REST and GraphQL automation framework built on httpx and JSON Schema validation, designed for functional, regression, and smoke coverage with fast developer feedback.
Shared Python core package for automation frameworks, centralizing reporting models, events, artifacts, helpers, and reusable patterns across web, mobile, and API suites.
Evaluation suite for LLM-based products, focused on hallucination detection, RAG faithfulness, semantic similarity, prompt checks, and LLM-as-a-judge scoring.
Understand the product, users, technical stack, release risks, and business goals before choosing what to automate.
Design and build the framework, tool, or evaluation pipeline around your stack, workflows, and team skill level.
Deliver clean documentation, examples, CI integration, and a handoff your team can actually use from day one.
Support the rollout, tune flaky areas, improve coverage, and evolve the solution as your product changes.
A US and Canada vehicle-auction platform relied on slow, costly cloud testing and an aging Cypress suite that was difficult to scale, maintain, and trust.
Migrated the suite from Cypress to Playwright with Python, then built an in-house testing lab with real servers and devices to replace external cloud testing.
faster regression runs, stronger reliability, lower cloud-testing cost, and full control over the test environment.
Have a testing challenge or need a framework built from scratch? Let's talk through the problem and map out a practical path forward.