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Recommendation deserves evidence based accuracy
Testiva delivers specialist QA for AI Recommendation Systems ranking accuracy, personalisation quality, bias testing, and regression testing after every update.
500+
Recommendation Scenarios Tested
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when AI Recommendation Systems
What happens when AI Recommendation Systems
aren't tested properly
Biased recommendations harm specific user groups
Models trained on imbalanced data systematically surface different quality recommendations for different demographic groups, creating disparity that attracts regulatory scrutiny and user attrition.
Popularity bias drowns out relevant long-tail items
Engines that amplify already-popular items reduce catalogue diversity and fail users with niche preferences, undermining the business value of having a large item catalogue at all.
Silent quality degradation after model retrains
A retrained model can improve average metrics while degrading performance for specific segments, and aggregate A/B results won’t reveal which segments were harmed until churn data surfaces it.
Next-best-action errors drive wrong downstream decisions
Incorrect action recommendations in commercial or clinical workflows don’t register as model failures, they register as poor business or care decisions, making the root cause invisible without specialist evaluation.
How Testiva protects your platform
- AI recommendation expertise — Our QA engineers specialise in recommendation system evaluation, not generic software or LLM testing.
- Structured bias & fairness evaluation — We measure demographic parity, filter bubble risk, and popularity bias across user segments, and quantify the disparity gap.
- Ground truth ranking evaluation — We build annotated evaluation sets and measure precision, recall, NDCG, and MRR against your real user intent, not synthetic proxies.
- Next-best-action accuracy testing — Decision quality evaluated against annotated outcomes for every action type your recommendation engine produces.
- Automated regression on every retrain — We catch ranking degradation, parity regressions, and catalogue coverage drops after every model update before they reach production users.
What we test
Core components of an AI Recommendation System we cover
Every layer that affects recommendation quality, user fairness, and business outcomes is validated against models, catalogues, and user segments.
Ranking Accuracy & Relevance
Precision, recall, NDCG, and MRR measured against ground truth.
Personalisation Quality Testing
Recommendation relevance evaluated against user history and intent.
Bias & Demographic Fairness
Quality and diversity parity measured across user demographics.
Catalogue Coverage & Diversity
Long-tail exposure and popularity bias measured across surfaces.
Next-Best-Action Accuracy
Action recommendation correctness validated against ground truth.
Contextual Recommendation Testing
Quality evaluated across session context and real-time signals.
Regression & Retrain Testing
Ranking accuracy and parity re-evaluated after every retrain.
Security & Data Governance
Data access boundaries and PII exposure in signals verified.
Performance & Scalability
Inference latency and ranking consistency 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 test coverage and deliver a tailored evaluation strategy with annotated ground truth datasets.
Test Execution
Ranking evaluation, bias audits, and regression tests, with every finding logged with full reproduction steps.
Report & Iterate
A detailed report with ranking metrics, bias findings, and recommendations for the next model iteration.
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.
AI Recommendation System
Testing Packages
| Feature | Starter | Professional | Enterprise | Custom AI |
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| CORE FUNCTIONAL TESTING | ||||
| End-to-end recommendation pipeline testing |  |
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| Ranking accuracy & relevance evaluation | |
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| Personalisation quality testing | |
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| Concurrent query & load testing |  |
5K users | 10K users | Unlimited |
| Automated regression test suite | |
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Setup | Full build |
| RECOMMENDATION-SPECIFIC TESTING | ||||
| Ground truth dataset build & annotation | |
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| Precision, recall & NDCG measurement | |
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| Next-best-action accuracy evaluation | |
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| Contextual recommendation testing | |
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| Cold start & new user scenario testing | |
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| AI QUALITY & FAIRNESS | ||||
| Bias & demographic fairness evaluation | |
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| Filter bubble & echo chamber detection | |
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| Popularity bias measurement | |
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| Output consistency & regression monitoring | |
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| SECURITY, PRIVACY & COMPLIANCE | ||||
| PII detection in recommendation signals | |
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| GDPR / CCPA compliance testing | |
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| SUPPORT & REPORTING | ||||
| Dedicated recommendation 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
Ranking accuracy, personalisation quality, bias across user demographics, and next-best-action correctness, none of which appear in standard functional or UI test suites.
We build annotated ground truth datasets and evaluate recommendation outputs against user intent across segments, measuring precision, recall, NDCG, and MRR at production query volumes.
We measure recommendation diversity and demographic parity, flagging patterns where specific user groups receive systematically worse outputs, and quantifying the disparity gap per segment.
We evaluate recommended actions against annotated ground truth and measure the business or operational impact of incorrect suggestions across every action category your system produces.
We run regression evaluations against validated accuracy baselines after every change, detecting ranking shifts, demographic parity regressions, and catalogue coverage drops before they reach users.
We measure inference latency, throughput capacity, and ranking accuracy consistency under concurrent user loads at production traffic levels, with p95 latency benchmarked against your SLA requirements.
Get in touch
Start with a free Recommendation System QA audit.
Tell us about your recommendation system 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