Testing &
Validation
AI you can stand behind. Test for hallucinations, bias, adversarial inputs, and performance degradation before users ever see it.
AI systems fail in ways traditional software does not. Hallucinations, bias, adversarial inputs, degraded performance over time: these are not edge cases. They are the normal operating conditions of probabilistic systems deployed at scale.
Traditional QA is not enough. We test AI systems with AI-specific methodologies: red-teaming, bias audits, regression testing against evolving models, and safety validation that goes beyond functional correctness. The result is AI you can stand behind with documented confidence, not hope.
Confidence comes from evidence, not optimism.
How we validate AI systems.
We do not test AI the way you test traditional software. Probabilistic systems require probabilistic testing: methodologies designed for systems that can fail silently, degrade gradually, and behave differently under different conditions.
The result is documented confidence: evidence that your system performs as expected, and clear understanding of where it does not.
01
Functional testing
Systematic verification that the AI system produces correct outputs across the full range of expected inputs. We build comprehensive test suites that can be re-run as the model evolves.
02
Regression testing
Automated regression pipelines that catch performance degradation before it reaches users. Every model update is tested against the full historical benchmark suite before deployment.
03
Red-teaming & adversarial probing
Deliberate attempts to break your AI system: through jailbreaks, prompt injection, unusual inputs, and edge cases your users will eventually discover. We find the failures so your users don’t have to.
04
Bias & fairness audits
Structured evaluation across demographic groups, use case scenarios, and language variations to surface unintended disparities in AI outputs. Includes remediation recommendations.
05
Performance benchmarking
Latency, throughput, and reliability testing under simulated production load. We establish baselines and identify the performance envelope your system operates within.
06
Safety validation
Evaluation against safety criteria relevant to your use case: harmful content generation, data leakage, inappropriate actions by agentic systems, and regulatory compliance requirements.
What this work produces.
Test plan
Comprehensive document defining testing scope, methodology, acceptance criteria, and the risk areas being prioritized. Agreed before testing begins.
Functional test suite
An automated, repeatable test suite covering the full functional scope of your AI system. Reusable for every future deployment and update.
Red-team report
Detailed findings from adversarial testing, including specific vulnerabilities, severity ratings, reproduction steps, and recommended mitigations.
Bias audit
Documented assessment of model behavior across demographic and contextual variables, with findings and recommendations for remediation.
Benchmark results
Quantified performance metrics across accuracy, latency, reliability, and safety. Establishing the documented baseline for future comparisons.
Validation certificate
A formal sign-off document summarizing what was tested, what was found, and the conditions under which the system is considered ready for production.