You can't unit-test a language model.
AI isn’t deterministic — the same input can give a different answer, and a model that was 94% accurate can drift without a line of code changing. We test AI like the probabilistic system it is: evals against ground truth, red-teaming for safety, and monitoring for drift — on the AI you built or the vendor’s you bought. Senior teams, fixed scope, evidence you can defend.
180+ teams shipping with our engineers · ISO 27001-secure · NDA on request
AI eval report animated mockup
eval report · support-copilot v4
scoring…
— What we build
Four ways we stress-test your AI.
From an accuracy eval to an adversarial red-team run, we test the whole AI system — model, prompts, retrieval, guardrails and tools — and keep testing it in production, where the failures actually show up.
Quality & accuracy evals
Score outputs against a ground-truth set — faithfulness, relevance, correctness — instead of a gut feel.
Ground truth
LLM-as-judge
RAGAS
Regression
Red-teaming & safety
Adversarial testing for jailbreaks, prompt injection, data leakage and unsafe outputs — the whole deployment, not just the model.
Jailbreaks
Prompt injection
RAG poisoning
OWASP LLM Top 10
Bias, fairness & compliance
Fairness testing and audit-ready evidence mapped to the frameworks you answer to.
Bias
Fairness
NIST AI RMF
EU AI Act
Drift & production monitoring
Continuous eval in production, so silent degradation surfaces before your users feel it.
Drift
Monitoring
Alerts
Feedback loops
- Featured · Ground truth, not vibes
Looks good to me"
is not a test.
Most teams validate an LLM by eyeballing a dozen outputs and shipping. That’s not evaluation — it’s optimism. We build a labeled evaluation set, score every version against it with clear thresholds, and re-run it on every prompt and model change — so “it got better” is a number, not a feeling.
Labeled eval set
Scored thresholds
LLM-as-judge + human
Regression on change
Owned failures
1
BUILD
A ground-truth eval set for your use case
2
TEST
Maintainable tests, layered as a pyramid
3
MONITER
Keep evaluating in production, catch drift
Outcome
14 YEARS · 300+ PRODUCTS
14 YEARS · 300+ PRODUCTS
Built to catch it,
before your users do.
19+
Chatbots shipped
across 9 industries
14yrs
Years in business
founded 2012
95%
Engagement extension
clients renew or expand
<2wk
Kickoff time
brief → first standup
Capabilities & stack
Boring tech that
ships on time.
We test with the evaluation and red-teaming frameworks the field actually uses — automated for scale, human for judgment — and map the results to the standards you’re measured against.
How we pick
01
Ground truth first
No eval without a labeled set. A benchmark you can’t point to isn’t a benchmark — it’s a hunch with a percentage on it.
02
Automated for scale, human for judgment
Automated evals cover the huge combinatorial space; human review catches the subtle failures a metric quietly passes.
03
Test the deployment, not just the model
Prompt injection and tool abuse live in the whole system — retrieval, guardrails, agent actions — so we test end-to-end, not one prompt at a time.
04
Continuous, not one-time
A model that passed at launch drifts as data and usage shift. We keep evaluating in production, not just before it.
EVALUATION
DeepEval
RAGAS
Promptfoo
LangSmith
Braintrust
Ground-truth sets
RED-TEAMING
Garak
PyRIT
DeepTeam
OWASP LLM Top 10
Jailbreak suites
ML VALIDATION
scikit-learn
Cross-validation
Confusion matrix
Precision / recall
Giskard
MONITORING
Arize / Phoenix
Evidently
Drift detection
Feedback capture
Alerting
STANDARDS
NIST AI RMF
EU AI Act
ISO 42001
MITRE ATLAS Model cards
Test data mgmt
Industries
AI testing, tuned
for your industry.
Every domain has a different worst case — a wrong diagnosis, a biased decline, a leaked record. We prioritize the evals and red-team scenarios around the failure you can least afford.
Healthcare
Testing for AI that touches patient care.
- Clinical-accuracy and safety evals
- Bias testing on patient data
- Audit evidence for regulated AI
Finance & Fintech
Testing an auditor and a regulator will accept.
- Fairness testing on credit and risk models
- Red-teaming for data leakage
- Explainability evidence for review
Retail & E-commerce
Testing customer-facing AI before it speaks.
- RAG-answer and recommendation quality evals
- Toxicity and safety on customer-facing bots
- Scored A/B eval on every model change
Logistics
Testing the models the operation depends on.
- Accuracy evals on forecasting and extraction
- Robustness on messy real-world inputs
- Drift monitoring in production
Engagement models
Pick how you want to
ship with us.
Three ways in — all senior teams, all fixed against the outcome we agree before the first test case.
Fixed-scope
Pipeline & QA audit
Most popular Best for standing up evaluation you can run on every change.
- Scope → eval harness + red-team suite in 3–8 weeks
- Fixed scope, fixed price — no hourly drift
- Ground-truth set, scored gates, wired into your pipeline
- Documentation and a suite your team can own
Most popular
Project-based build
Best before you ship AI into production or an audit.
- Fixed price, 1–2 weeks
- We evaluate quality, safety and bias on your use case
- A scored report and a prioritized risk map
Embedded
Staff augmentation
Plug senior AI eval and red-team engineers into your team.
- 8+ yr average, hand-picked, never on a bench
- Evals, adversarial testing and monitoring — and the skills to fix what they find
- Direct line to your AI or data lead
FAQ
The questions everyone asks before kickoff.
Pipeline red half the time and nobody looks? Book a 30-min call with a senior engineer.
Why can't we test our AI like normal software?
Because normal software is deterministic — same input, same output, clean pass or fail. An AI model is probabilistic: the same prompt can give different answers, “correct” is a spectrum, and quality shifts as data and usage change. So instead of assertions, you need evals: scoring outputs against a labeled set with thresholds, plus adversarial testing and ongoing monitoring.
How do you actually measure whether an LLM is "good"?
We define what “good” means for your use case as a rubric — faithfulness, relevance, correctness, safety — then build a labeled ground-truth set and score every version against it, using automated judges for scale and human review for the subtle calls. The result is a number you can track and defend, not a vibe.
What's AI red teaming, and do we need it?
Red teaming is adversarial testing — deliberately trying to make your AI misbehave: jailbreaks, prompt injection, leaking data, producing unsafe output, or abusing tools. If your AI is in production, customer-facing or regulated, you need it — both to reduce real risk and increasingly to satisfy standards like the EU AI Act and OWASP’s LLM Top 10.
Can you test AI we didn't build — a vendor's model?
Yes. Independent validation is a core part of what we do. We can evaluate and red-team a third-party or bought-in model against your requirements, so you know what you’re deploying before you stake your name on it.
How do you test whether a RAG system is hallucinating?
By measuring faithfulness — whether each answer is actually supported by the documents it retrieved — alongside answer relevance and retrieval quality. If the model states something the sources don’t support, the eval catches it, on a set of cases, not one lucky demo.
How do you keep it accurate after launch?
We set up continuous evaluation and drift monitoring in production, so when live inputs move away from what the model was validated on — or quality quietly slips — you get an alert, not a customer complaint.
Do you cover compliance — EU AI Act, NIST, ISO 42001?
We map testing and reporting to NIST AI RMF, the EU AI Act, OWASP’s LLM Top 10 and ISO 42001, and deliver audit-ready evidence aligned to them. We’re ISO 9001 and 27001 certified ourselves, so the process around your data is audited too.
How is this different from your Continuous Testing and Model Development pages?
Continuous Testing covers deterministic software (clean pass/fail in CI/CD). Model Development is where we build a model. This page is validating AI behavior — quality, safety, bias, drift — which needs different methods because AI isn’t deterministic. On the first call we’ll point you to the right one.
ISO 27001-secure · Certified since 2012
Let's prove your AI, worth not just ship it.
Send your brief — a senior engineer (not a sales rep) replies within 24 hours with a read on how your AI is validated today, the gaps, and a fixed-scope plan.
- A 30-min scoping call with a senior AI engineer
- A read on how your AI is validated today — and the gaps
- A sample eval and a red-team probe on your use case
- A fixed-scope plan + an eval blueprint — yours to keep
