Ship AI features your users can actually trust.
AI doesn’t fail like normal software. A model can give a different answer every time, hallucinate facts with total confidence, leak data through a clever prompt, or quietly degrade the moment you change a prompt or swap a model. We test LLMs, chatbots, copilots, RAG systems, and AI-powered products for accuracy, safety, and reliability — and we build the evaluation pipelines that keep them dependable as they evolve. Whatever your AI needs, we implement testing around it.
Why AI needs a different kind of testing
- Non-deterministic outputs — the same input can produce different results
- Hallucinations — confident, plausible, and completely wrong
- Prompt injection & jailbreaks — an entirely new security attack surface
- Bias, toxicity & safety risks — reputational and compliance landmines
- Silent regressions — quality drifts when you tweak a prompt or change models
- “Quality” is hard to define — what does a good answer even mean?
What we test
- LLM Output Quality & Accuracy — graded against expected behavior with evals and golden datasets
- Hallucination & Factuality Testing — catch confident-but-wrong answers before users do
- RAG & Retrieval Accuracy — is the model genuinely grounded in your data?
- Prompt Injection & AI Red-Teaming — adversarial testing for jailbreaks and data leaks
- Bias, Toxicity & Safety — fairness checks and guardrail validation
- Regression Testing for Prompts & Models — catch quality drift whenever anything changes
- Performance, Latency & Cost — token usage, response time, and throughput under load
- Conversational & UX Testing — does the assistant actually help real users?
How we test AI
- Evaluation suites (evals) built on golden datasets with automated scoring
- LLM-as-judge plus human review for the nuanced quality machines miss
- Adversarial red-teaming for safety, jailbreaks, and data-leak risks
- CI/CD integration so every prompt and model change is checked before it ships
- Dashboards & metrics that turn “AI quality” into something you can actually see and track
Whether you’re building a customer-facing chatbot, an internal copilot, a RAG system, or an agentic workflow, we tailor the testing approach to your product, your risks, and your stack.
Why teams trust Masters of Testing
- Engineers fluent in modern AI stacks — LLMs, RAG, agents, and the tools around them
- Transparent, real-time reporting — no black boxes
- Secure practices and signed NDAs to protect your models, prompts, and data
- Trilingual delivery (English, Russian, Arabic) for global teams
Frequently asked questions
Why can’t I test AI with normal QA?
Traditional tests expect the same input to give the same output. AI is non-deterministic and open-ended, so it needs evaluation-based testing — scoring quality, accuracy, and safety across many cases — rather than simple pass/fail assertions.
How do you test something non-deterministic?
We build evals: curated datasets of inputs with expected behavior, scored automatically (and with LLM-as-judge plus human review) so you get a measurable quality signal even when exact outputs vary.
Can you catch hallucinations?
Yes — factuality and grounding tests flag confident-but-wrong answers, and for RAG systems we verify the model is actually using your retrieved data rather than inventing it.
Do you do AI security testing?
We run adversarial red-teaming for prompt injection, jailbreaks, and data-leak risks, and validate that your guardrails actually hold under pressure.
Which models and stacks do you work with?
The major hosted models and open-source ones alike, plus the surrounding stack — RAG pipelines, vector databases, agent frameworks, and your own fine-tuned models.
Can you integrate AI testing into our CI/CD?
Yes. We wire evals into your pipeline so every prompt, model, or config change is automatically checked for quality and safety regressions before it ships.
How do we measure AI quality over time?
Through clear, tracked metrics — accuracy, hallucination rate, safety pass rate, latency, and cost — reported on dashboards so quality trends are always visible.
We’re just starting with AI — can you help early?
Absolutely. The earlier we set up evaluation and guardrails, the cheaper quality is to maintain. We’ll help you build the right testing foundation from day one.
Ready to make your AI dependable?
Let’s talk about your AI product and where the risks are. It’s free, and the whole conversation is about you.
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