AI-powered features don't fail like traditional software. A prompt that works perfectly today can produce a wildly different answer tomorrow, a model can be subtly biased in ways no functional test catches, and "correct" often isn't binary. Our AI Testing service brings structured, repeatable evaluation to AI/ML-powered applications, LLM and chatbot integrations, and the prompts, pipelines, and APIs behind them, so you can ship AI features with the same confidence you expect from the rest of your product.
Specialized testing for AI/ML features, LLMs, and chatbots, covering accuracy, consistency, safety, bias, and regression in ways traditional test scripts can't.
Test checklist
What we test
AI features fail in different ways than traditional software. Here's how we cover the full surface area, and there's more we tailor per engagement.
Functional testing for features built on machine learning models, recommendation engines, classifiers, generative pipelines, treating the model as a testable component of the system, not a black box.
Conversation-flow testing for chatbots and virtual assistants, covering intent recognition, context retention, multi-turn conversations, and graceful handling of off-topic or adversarial input.
Structured testing of prompts against a broad matrix of inputs to confirm responses stay accurate, relevant, and on-brand as prompts, models, or context windows change.
Running the same or similar inputs repeatedly to measure how stable and reliable outputs are, and catching hallucinations before they reach a user.
Deliberately probing with adversarial, ambiguous, and out-of-scope inputs to see how the system behaves at its edges, not just on well-formed happy-path prompts.
A repeatable evaluation harness that reruns your test matrix every time a model, prompt, or fine-tune changes, so quality doesn't quietly drift over time.
Structured evaluation across demographic and contextual variations to surface skewed, stereotyped, or inequitable outputs before they reach users.
Reliability, latency, cost, and error-handling testing for the APIs powering your AI features, including third-party model providers.
Multimodal (voice, image, video) evaluation, agentic workflow testing, retrieval-augmented generation (RAG) accuracy, and other AI testing needs scoped to your specific product.
How we evaluate AI features
AI systems don't stay still, so the evaluation process is built to run again and again, not just once before launch.
We define what "good" looks like for your feature, accuracy thresholds, tone, safety boundaries.
Test matrices built from real user intents, edge cases, and adversarial inputs.
Automated scoring for scale, paired with human review for nuance and judgment calls.
A dedicated pass for harmful, biased, or unsafe outputs against your defined criteria.
The full suite is wired to rerun automatically whenever a model, prompt, or fine-tune changes.
Common questions
Yes. Most AI testing engagements are exactly this, evaluating how your product uses a third-party model: your prompts, your guardrails, your integration, and the end-to-end user experience, rather than testing the model provider's infrastructure itself.
Keep exploring
Tell us where you are in the product lifecycle and we'll recommend the right mix of testing, consulting, and enablement.