Our Lead Differentiator
Your AI Feature Will Fail in Ways Traditional QA Was Never Built to Catch
Hallucinations. Prompt injection. RAG systems that retrieve the wrong context with total confidence. We built a Testing practice specifically for how AI breaks.
The Practice
Eight Disciplines for How AI Actually Breaks
LLM Evaluation & Benchmarking
Measured output quality across model and prompt versions, so 'better' is a number, not a feeling.
Prompt Testing & Validation
Regression suites for prompts, so a wording tweak can't silently break production behavior.
RAG Pipeline Testing
Retrieval accuracy, context relevance, and source grounding validated against golden datasets.
AI Agent Testing
Multi-step task correctness, tool-use validation, and failure recovery for agentic systems.
Hallucination Testing
Systematic detection of confident-but-wrong answers before a customer sees one.
Bias Testing
Structured probes for demographic, topical, and positional bias in model outputs.
AI Security Testing
Prompt injection, jailbreaks, and data-leakage attempts run against your system before attackers try them.
Multimodal & Voice AI Testing
Image, audio, and voice interface validation beyond text-only checks.
Why It Matters
“It Works in the Demo” Is Not the Same as “It Works.”
AI features fail quietly. Traditional QA checklists don't catch this. We built a practice that does.
The demo worked. Production didn't.
A prompt change three sprints ago quietly degraded answers for an entire user segment. Nobody had a regression suite for prompts.
The RAG system retrieved the wrong context, confidently.
Retrieval looked fine in spot checks. Against a golden dataset, accuracy told a different story.
The agent completed 9 steps and failed the 10th, silently.
Multi-step workflows fail in the seams. Tool-use validation catches what end-to-end eyeballing can't.
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
AI quality engineering is the practice of Testing AI and LLM features, prompt reliability, RAG retrieval accuracy, agent behavior, and hallucination rates, going beyond traditional software QA, which was never designed for non-deterministic systems.
Put Your AI Feature Through Real Testing
30 minutes with our AI QA practice, bring your hardest failure case.
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