Our Lead Differentiator
Your AI Feature Will Fail in WaysTraditional 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 numbers we put on AI quality, measured on every prompt, model, and index change.
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.
One agent run
Nine steps right, one step wrong, and the task still reports success
This is what “the agent completed nine steps and failed the tenth, silently” looks like. The run finished. The user got an answer. The answer quoted the wrong price.
- 01parse requestintent + entities extracted
- 02search knowledge base8 candidate documents
- 03rank contexttop 3 passed relevance floor
- 04call pricing API200 OK, 42 ms
- 05call inventory API200 OK, 61 ms
- 06reconcile recordsno conflicts
- 07draft responsewithin length budget
- 08policy checkno restricted terms
- 09format for channelvalid markdown
- 10apply customer discounttool returned null — agent answered anyway, quoting list price
The task ran to completion and produced a well-formed answer, so the end-to-end check is green. This is the assertion most teams have.
Illustrative agent run. The failure shape is the one described above, not a specific client system.
Why AI features need a different assurance model
Conventional software fails loudly.
Conventional software fails loudly. It throws an exception, returns a 500, renders a blank screen, something observable happens and something alerts. An LLM feature fails quietly: it returns a fluent, well-formatted, confident answer that is wrong. No error is raised, no status code changes, and the user, having no way to know, believes it. You find out from a support ticket, a screenshot on social media, or a customer who quietly stops trusting the product.
That single property invalidates most of the QA toolkit. Exact-match assertions are useless against non-deterministic output. Coverage percentages mean nothing when the input space is natural language. Manual spot-checking, which is how the overwhelming majority of shipped AI features are currently judged, samples a handful of cases from an effectively infinite space and calls it verification. The failure modes are catalogued in 5 Ways LLM Features Fail Silently.
The practice: define correct, then measure it continuously
Everything starts with a golden dataset, real inputs paired with agreed expected outputs, built from your actual traffic and versioned alongside the product.
Everything starts with a golden dataset, real inputs paired with agreed expected outputs, built from your actual traffic and versioned alongside the product. This is the hardest and most valuable part of the engagement, because it forces an organisation to state explicitly what a good answer is. Teams routinely discover during this step that product, engineering and support each held a different definition.
On top of that sits an evaluation harness, typically in Python, scoring every change against the dataset: factual accuracy, grounding in retrieved sources, refusal behaviour where refusal is correct, format and schema compliance, latency and cost per request. It runs in CI, and thresholds fail a build on meaningful regression. That is what turns a prompt edit from a hopeful change to a string into a reviewable engineering event with a measurable effect.
What we evaluate, by pattern
RAG pipelines: retrieval recall and precision, chunking strategy, grounding enforcement, citation accuracy, and behaviour when retrieval returns nothing relevant. The full method is in How to Test a RAG Pipeline.
Tool-using agents: task completion, wrong-tool selection, argument validation, permission boundaries, loop and cost limits, and recovery after a failed step. Checklist: Testing AI Agents.
Structured extraction: schema compliance, repair paths for malformed output, and precision on the fields a downstream system will act on without a human in the loop.
Conversational features: context handling across turns, memory boundaries, safety and refusal behaviour, and graceful degradation when the provider is slow or rate-limited.
Adversarial robustness: prompt injection, jailbreak attempts, and hostile or nonsense input, tested deliberately rather than discovered by users.
Cost and latency: tracked as first-class quality attributes, because an accurate feature nobody can afford to run is not shipped.
Where this sits alongside conventional Testing
An AI feature is still software.
An AI feature is still software. It has an API, a database, a UI, authentication and a deployment pipeline, all of which need the conventional disciplines applied normally. The evaluation practice sits on top of that rather than replacing it, and the two are usually run by the same engagement so that a failure is diagnosed once rather than argued about between two vendors.
If you are building the feature as well as assuring it, our LLM integration engineering and software Development practices ship with the evaluation layer attached from the start, which is considerably cheaper than retrofitting it after the first incident.
Engagement path
How the engagement runs
Define correct
A golden dataset built from your real traffic, with expected outputs agreed across product, engineering and support. Nothing measurable exists before this.
Baseline where you are
The harness scores the live feature so you know today's accuracy, grounding, refusal and cost, usually the first time anyone has a number.
Gate the pipeline
Thresholds wired into CI so a prompt, model or retrieval change that degrades quality fails a build like any other defect.
Monitor and expand
Production sampling, drift detection and dataset growth from real failures, so the evaluation gets stronger as the product does.
Deliverables
What you get
Artefacts you keep and can run without us. Everything lives in your repositories and your tooling.
Handover pack
6 artefacts · yours to keep
A versioned golden dataset built from your real traffic
An evaluation harness scoring accuracy, grounding, refusal, latency and cost
A written baseline of where the feature stands today
CI thresholds that fail builds on meaningful quality regression
Adversarial and prompt-injection coverage, plus empty-retrieval behaviour
Production sampling and drift detection your team runs without us
Self-check
Signs your team needs this
If more than one of these is true, it is usually cheaper to fix now than after the next release.
Quality is judged by spot-checking outputs before a release
Prompt changes ship with no regression signal whatsoever
Nobody can say whether the last model update improved or degraded the product
Token spend is unpredictable and unowned
The feature has never been tested against adversarial or empty-retrieval inputs
Your QA team is strong and has never been asked to test something non-deterministic
A Senior Engineer replies within one business day. NDA first.
Questions
Frequently Asked Questions
Straight answers, written the way we'd say them on a call.
Still curious? Talk to usKeep reading
Where to go next
The services and resources teams read next.
Put Your AI Feature Through Real Testing
30 minutes with our AI QA practice, bring your hardest failure case.
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