AI-augmented testing, agent development, LLM alignment and red teaming — practical AI services from a team that has spent years testing software for a living, now applied to the systems building it.
Getting an AI feature from prototype to production raises questions most QA processes were never built to answer: is the training data actually representative? Will the model behave the same way in six months? What happens when someone deliberately tries to break it? We work on exactly these questions — as testers first, AI specialists second.
Ten services covering the full lifecycle of an AI system — from the data that trains it to the attacks that try to break it.
We bring AI into your existing test automation practice — smarter test generation, self-healing locators and AI-assisted triage — without throwing away the frameworks your team already knows.
Learn moreDesign and build task-automation agents with LangChain, Microsoft AutoGen and similar frameworks, wired into your existing systems for real workflows — not demos.
Learn moreHelp your product team ship AI-powered features — from API design to integration — with the same testing discipline we bring to everything else.
Learn moreLabelling, annotation and dataset curation for your AI/ML models, with an eye for the bias and gaps that quietly wreck model performance later.
Learn moreUse AI coding assistants responsibly inside your CI/CD pipeline — faster refactors and code review, with the validation steps that keep quality intact.
Learn moreWe test the pipelines and datasets feeding your AI systems, not just the model output — accuracy, consistency and drift, caught before they become a production problem.
Learn moreValidate your AI system's behaviour against the constraints you actually care about — safety, fairness and staying on-topic — before your users find the edge cases for you.
Learn moreFine-tuning, evaluation frameworks and ongoing tuning to keep an LLM-based feature aligned with what your business actually needs it to do.
Learn moreWe build retrieval-augmented generation systems that answer from your own data, with the retrieval quality and evaluation loop that keeps answers grounded.
Learn moreAdversarial testing for prompt injection, data leakage and jailbreaks — a structured attempt to break your AI system before someone else does.
Learn moreStructured to keep accuracy, scalability and ethical compliance in view at every stage — from the first data review to post-launch monitoring.
We're a QA-and-AI consulting team, not a generic offshore staffing pool — every engagement is scoped, not templated.
Our instructors are working QA and automation consultants at companies like Lacoste and TotalEnergies — the same people teaching AI testing also do the work.
No off-the-shelf package: every engagement starts with understanding what you've already built, not a generic playbook.
Playwright, LangChain, common LLM evaluation frameworks — the tools this space actually uses now, not a fixed toolkit from three years ago. Read how to test an LLM chatbot or a RAG app in 2026 and the five AI testing changes that are actually shipping in 2026.
Designed to integrate with your CI/CD pipeline and existing QA process, not replace it wholesale.
We help you design toward GDPR-conscious, fairness-tested AI. We won't claim a certification we don't hold.
The same rigor behind our ISTQB-aligned training extends directly to how we test AI systems.
From the first scoping call to post-launch iteration, the same team stays engaged with your project.
Need your own team to learn this instead of outsourcing it? The same expertise is available as hands-on training.
Tell us what you're building and where it stands today — we'll tell you honestly whether we can help.
Discuss your AI projectTen services: AI-augmented test automation, AI agent development, AI feature engineering, training data services, AI-augmented development, data integrity testing, guardrail testing, LLM alignment and optimisation, RAG application development and AI red teaming.
No. We bring AI into your existing test automation practice without throwing away the frameworks your team already knows.
The data and pipelines that feed it (accuracy, consistency, drift), its behaviour against guardrails, and its resistance to deliberate attempts to make it fail (red teaming).
Written by Oussama Belakhdar, founder of AutomationDataCamp. Last updated .