AI GroundTruth
Know exactly how your AI system behaves in the real world
AI GroundTruth by Global App Testing puts your AI system in front of a structured global crowd, across languages, cultures, and edge cases your team hasn't thought of yet.
Real humans. Real signal. Real confidence before launch.
Get started with AI GroundTruth, our new evaluation service, today.
Move faster
Release safely
Deliver local robustness
Segment more deeply
Get your evaluation scoped now
Tell us what you're building. We'll come back with a tailored crowd profile, scenario outline, and a fixed price.
Most proposals turned around in 5 business days.
AI Groundtruth is built for teams who:
- Are shipping AI to global markets in the next 90 days
- Need evidence of safety and quality for buyers, boards, and/or regulators
- Can't rely on internal evals to catch what real users will
What's the problem
The biggest risks in AI don't show up in internal evals. They show up in public.
AI teams invest in benchmarks, RLHF, internal red-teaming, synthetic prompts, and safety reviews. But when AI reaches global users, enterprise buyers, and regulators, hidden behavioural gaps emerge.
- Reputational backlash spreads instantly
- Enterprise deals slow or stall
- Legal teams escalate
- Product teams revert to reactive firefighting
Who are the crowd?
Unlock a track record of supporting the world's greatest AI businesses
We know that every human touchpoint in your supply chain requires smart governance to reduce risk. Here's how smart crowd management reduces risk for you.
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We're helping a major AI lab scaling to billions of users to drive their local market share
We delivered local adversarial exploration and cultural alignment reviews to tackle hallucinations, offensive content, and sensitive prompt failures — helping them confidently launch new model versions worldwide and continuously adapt their models for local users.
We helped a client with an AI support bot to validate their feature was ready-to-launch in multiple markets
We conducted real-world prompt evaluation across multiple languages to ensure AI outputs remained helpful, polite, and brand-consistent — across complex enterprise workflows in a local and specific domain setting. This meant that the organization was able to release their feature quickly knowing that the necessary tests had been done.
We helped a major consumer application give their users a creative moment safely
We supported a global social platform as it introduced an AI-generated creative feature across multiple markets, gathering diverse human feedback to assess cultural resonance, appropriateness, and user perception before broader rollout.
We support both innovators and integrators to deliver safer, more competitive global products
Global App Testing works with businesses who are pioneering core technologies and businesses integrating into their stack
Innovators
Conquer global markets at scale with deeper user fit
Build the foundation for long-term strategic optimization and discover the strategic value of real-life to your market domination.
Integrators
Get your AI feature to market safely and robustly
Integrating AI into your existing product or tooling? Get your AI product to market quickly, safely, and effectively via our integration suite.
GAT for first-movers
AI GroundTruth takes tried-and-tested evaluation to a global user-simulated audience
Product leaders building foundational GenAI technologies need confidence that their models work everywhere, for everyone. GAT helps innovators validate performance across languages, cultures, and real-world contexts, turning global human diversity into a strategic advantage for building AI that scales responsibly, safely, and at speed.
- Stress-test at global scale
- Localise intelligence, not just interface
- Surface cultural blind spots early
- Benchmark across diverse user expectations
- Enhance multilingual robustness
- Continuously monitor model drift
Route best-in-class AI evaluation techniques to a domain-specific audience
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Crowd participants provide structured human input throughout model development cycles. Instead of relying solely on internal reviewers, innovators gather feedback from diverse users across regions and demographics. This broad perspective ensures outputs are shaped by real-world expectations, behaviours, and context, supporting faster iteration and more representative performance.
Large and diverse groups of contributors generate comparative judgments and qualitative signals that inform reinforcement learning processes. Broader participation reduces reliance on narrow samples, strengthening alignment signals across cultures and user types. This helps models reflect more globally representative preferences while maintaining scalability in feedback collection.
Crowd contributors compare outputs and rank them based on quality, usefulness, tone, or clarity. Aggregated rankings reveal patterns across regions and demographics, helping product leaders understand how different audiences perceive performance. These insights guide tuning decisions using structured human preference data at scale.
Diverse participants explore prompts across varied real-world scenarios, highlighting ambiguity, inconsistency, or unexpected behaviour. By capturing feedback from different interaction styles and linguistic backgrounds, innovators gain practical insight into how prompts perform outside controlled environments, supporting more reliable and adaptable prompt design.
Global contributors assess outputs against defined safety and policy criteria, flagging harmful, misleading, or sensitive content. A geographically distributed crowd brings awareness of local norms and regulatory differences, helping surface region-specific risks through structured human review processes.
A diverse crowd exposes models to varied demographic and cultural perspectives, identifying outputs that feel exclusionary or stereotypical. Patterns emerging from aggregated feedback highlight inconsistencies across groups, helping innovators uncover bias that may not appear within more homogeneous evaluation environments.
Local participants assess whether outputs resonate appropriately within their cultural context. This includes reviewing tone, assumptions, idioms, and references. Crowd diversity enables scalable localisation insight, helping ensure AI systems feel natural and relevant across markets rather than simply translated.
Participants intentionally probe systems with challenging or boundary-pushing prompts to reveal weaknesses. While not specialist red teams, diverse contributors bring varied curiosity, language use, and interaction styles. This broad exploratory approach helps surface unexpected behaviours before wider release.