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Generative AI deserves rigorous evaluation
Testiva delivers specialist QA for Generative AI output quality, hallucination detection, safety filtering, and regression testing across every modality.
10K+
Generation Outputs Evaluated
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when Generative AI Applications
What happens when Generative AI Applications
aren't tested properly
Hallucinated facts reach users at generation scale
Generated text that presents fabricated information confidently cannot be distinguished from correct output by format-based testing, only by evaluation against validated reference sources.
Harmful or unsafe outputs bypass content filters
Safety filters that perform well on standard prompts fail under adversarial conditions and indirect policy violations that only structured red-team testing surfaces before deployment.
Bias in generated outputs creates compliance and reputational risk
Models that produce systematically different quality or representation across demographic groups create regulatory exposure that aggregate quality metrics never capture.
Silent quality degradation after model updates and fine-tunes
A fine-tuned model can improve average benchmark scores while degrading quality on specific task types, and the regression stays invisible until users report it.
How Testiva protects your platform
- Generative AI evaluation expertise — Our QA engineers specialise in output quality evaluation, safety testing, and bias measurement across generation modalities, not generic software QA.
- Human-calibrated quality rubrics — We build evaluation frameworks with human-annotated quality standards for your generation task, measuring quality distributions rather than single-output snapshots.
- Structured red-team & adversarial safety testing — We run adversarial prompt suites, indirect policy violation scenarios, and edge case generation conditions that reveal the safety failures standard tests miss.
- Bias & fairness evaluation across generation outputs — We measure demographic representation and quality parity across generated content, flagging systematic disparities before they reach production users.
- Automated regression on every model update — Quality distribution baselines are established and monitored continuously, detecting output drift, tone shifts, and safety regressions after every fine-tune.
What we test
Core components of a Generative AI application we cover
Every layer that affects output quality, safety, fairness, and production reliability is validated against current benchmarks and deployment conditions.
Text Generation Quality & Accuracy
Factual accuracy and coherence evaluated across task types.
Image Generation Quality & Consistency
Prompt adherence and visual quality evaluated at scale.
Audio Generation Quality & Fidelity
Speech naturalness and artefact detection across speakers.
Video Generation Quality & Coherence
Temporal consistency and frame-level artefacts evaluated.
Hallucination & Factual Accuracy Testing
Claims verified against validated reference datasets.
Safety & Harmful Content Detection
Harmful output rates tested across red-team scenarios.
Bias & Representation Fairness
Representation and quality parity measured across content.
Regression & Quality Drift Testing
Quality distributions re-evaluated after every update.
Performance & Throughput Testing
Generation latency and throughput verified under load.
HOW IT WORKS
Up and running in 4 simple steps
From first contact to your first test report a process designed to be fast, transparent and low-friction.
QA Audit & Plan
We audit your current evaluation coverage and build a quality framework with human-calibrated rubrics for your task.
Test Execution
Output quality evaluation, safety red-teaming, bias audits, and regression tests, with every finding logged and rated by severity.
Report & Iterate
A report with quality distribution metrics, safety findings, bias analysis, and remediation recommendations.
What People Say
Testiva is a great team to work with. I’ve hired them multiple times and recommended them to others, all impressed by their thorough work. Highly recommended for QA.
Testiva team is highly skilled and extremely thorough. I trust them for accurate and timely delivery. They are a reliable resource for any project.
Testiva team delivered outstanding quality with great professionalism. Communication was excellent and delivery met expectations. Highly recommended.
Excellent team worked well with minimal supervision and did a great job. Their work helped us improve the robustness of the platform.
Generative AI Application
Testing Packages
| Feature | Starter | Professional | Enterprise | Custom AI |
|---|---|---|---|---|
| CORE FUNCTIONAL TESTING | ||||
| End-to-end generation pipeline testing |  |
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| Text generation quality evaluation | |
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| Prompt adherence & instruction-following | |
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| Concurrent generation & load testing |  |
5K requests | 10K requests | Unlimited |
| Automated regression test suite | |
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Setup | Full build |
| GENERATIVE AI-SPECIFIC TESTING | ||||
| Human-calibrated quality rubric build | |
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| Hallucination & factual accuracy testing | |
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| Image generation quality & brand safety | |
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| Audio generation fidelity & artefact detection | |
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| Video generation coherence & quality | |
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| Output consistency & variance scoring | |
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| AI SAFETY & FAIRNESS | ||||
| Harmful content & policy violation detection | |
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| Adversarial & red-team safety testing | |
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| Bias & demographic fairness evaluation | |
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| Prompt injection & jailbreak resistance | |
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| SECURITY, PRIVACY & COMPLIANCE | ||||
| PII detection in generated outputs | |
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| Copyright & IP leakage detection | |
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| GDPR / CCPA compliance testing | |
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| SUPPORT & REPORTING | ||||
| Dedicated generative AI QA lead | |
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| AI quality scorecard & weekly report | |
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| 24/7 critical defect SLA | |
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Common questions
Output quality, factual accuracy, hallucination detection, safety filtering, and format consistency, tested at production scale across every modality your application produces.
We use human-calibrated evaluation rubrics and measure quality distributions across repeated generations, not single-output snapshots, giving you statistically meaningful quality baselines.
We run structured adversarial and red-team test suites measuring refusal accuracy, harmful output rates, and bias across demographic groups, covering indirect policy violations that standard prompts never surface.
We evaluate visual output quality, prompt adherence, style consistency, and content safety across large generation batches, with brand safety criteria defined collaboratively before testing begins.
We compare output quality distributions before and after changes, detecting accuracy shifts, tone drift, safety regressions, and bias changes against the validated baseline established at engagement start.
We measure generation latency, throughput capacity, and output quality consistency under concurrent high-volume generation loads, benchmarked against your p95 latency and quality SLA requirements.
Get in touch
Start with a free Generative AI QA audit.
Tell us about your generative AI application and we’ll map out exactly what testing you need, no obligation, no sales pitch.
Email us
Book a call
30-minute discovery sessions available Mon–Fri
Fast response
We reply to all enquiries within 1 business day