QA for AI & LLM evaluation
AI systems aren't tested like regular software: they can hallucinate, bias or answer the same question differently. We evaluate your chatbots, agents and LLM features against batteries of real cases (accuracy, hallucinations, bias and consistency), with metrics, not intuition.
What is AI QA and LLM evaluation?
AI QA is the discipline of testing systems that use language models (chatbots, assistants, agents and LLM features) whose behavior is not deterministic. Unlike classic software, the same input can produce different, made-up or biased answers. We measure how reliable your AI is against real cases, with objective criteria and scores.
We design evaluation batteries (evals) that put your model through representative and adversarial prompts, and score each response for accuracy, hallucination, bias, tone, safety and consistency. We combine automated verification with expert human review and deliver where and how much your AI fails, with examples.
An AI that sounds convincing is not the same as a correct AI
LLMs generate plausible text even when it's false: they hallucinate data, leak information, answer the same question differently and carry biases. In production that's reputational, legal and trust risk. Evaluating your AI with real cases and metrics, before and after each model or prompt change, is the only way to know if it improves or worsens.
We design evaluation batteries of over 500 cases per product and score 100% of responses per criterion (accuracy, hallucination, bias, tone, safety), with a report of failures and examples within 24 hours.
What you gain by evaluating your AI methodically
Without AI evaluation
What you gain by evaluating your AI
We detect and measure when your model makes things up, so it doesn't reach production as if it were true.
We compare model and prompt versions with the same battery: you know if each change raises or lowers quality.
We evaluate bias, tone and consistency so your AI answers everyone equally well.
We give you objective quality and safety metrics to ship your AI without crossing your fingers.
Our process, step by step
Defining criteria and cases
runningWe agree what your AI should do and build a battery of real and adversarial cases with the expected answer.
Designing the eval battery
queuedWe prepare the evaluations and scoring rubrics: accuracy, hallucination, bias, tone, safety and consistency.
Execution and scoring
queuedWe run your model through the cases and score each response combining automated verification and human review.
Hallucination and bias analysis
queuedWe identify failure patterns (what it hallucinates, where it biases, when it contradicts itself) and measure their frequency and impact.
Report and continuous evaluation
queuedWe deliver the metrics with examples and leave the battery ready to re-evaluate on every model or prompt change.
We understand how models fail, not just how to use them
We're an ISTQB®-certified team that evaluates AI systems methodically: reproducible batteries, clear rubrics and human review. We know where LLMs hallucinate and bias, we build evals you can re-run in your CI and explain every result in your language, with the exact case and response.
What the service includes
Classic software testing vs. AI QA
Testing an LLM feature isn't like testing normal software. The root difference is determinism: classic software always returns the same output for the same input; a language model doesn't.
| Classic software | AI / LLM system | |
|---|---|---|
| Behaviour | Deterministic: same input, same output | Non-deterministic: the same input can vary |
| What 'correct' means | Pass/fail against an exact expected result | Scoring by criteria (accuracy, tone, safety) |
| Typical failures | Logic bugs, errors, crashes | Hallucinations, bias, unsafe or inconsistent answers |
| Method | Test cases with fixed assertions | Eval batteries with scoring and thresholds |
| Regression | Re-run the suite | Re-run evals: a model or prompt change can degrade silently |
If your product includes a chatbot, an assistant or any LLM feature, traditional testing isn't enough: reliability has to be measured statistically, with reproducible evaluation batteries and thresholds. For the rest of the application, classic tests still apply — and are still needed.
Frequently asked questions
Articles about this service
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Request an evaluation of your AI system
Tell us what AI you have (chatbot, agent or LLM feature) and what worries you. We reply in under 24h, in Spanish or English, with a tailored proposal.