AI GroundTruth

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 behnchmarks, 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

What's the solution?

You need to take action

The consequences will prevent you from winning the market

Consequences for your business

Reputational backlash

Revenue loss

Legal escalation

Enterprise buyers slow down

Launching unassured AI products can quickly damage credibility. Failures, biased outputs, or data issues attract media scrutiny and social backlash, eroding customer trust. Once confidence declines, the brand becomes associated with risk rather than innovation, making recovery costly and time-consuming for leadership.

Commercial impact often follows product instability. Customers may cancel contracts, delay renewals, or abandon pilots. Sales cycles lengthen as prospects demand reassurance, while refunds and remediation costs reduce margins. Short-term disruption can evolve into sustained revenue decline in competitive markets.

Unverified AI releases heighten legal exposure. Performance failures, compliance gaps, or data breaches can trigger investigations, contractual disputes, or regulatory penalties. Legal proceedings consume leadership attention, increase operational costs, and create public records that further undermine stakeholder confidence.

Enterprise buyers respond to instability with caution. Procurement teams introduce deeper security reviews, extended pilots, and stricter contractual safeguards. Decision cycles lengthen, budgets shift to safer alternatives, and expansion plans pause, slowing growth and weakening competitive momentum.

6 minutes

03 MARCH 2026 – The world stopped as Global App Testing announced AI GroundTruth

6 minutes

Read our press release

Move faster

Accelerate AI launches with streamlined local validation and workflows built for scale.

Release safely

Embed regulatory alignment, risk controls, and cultural nuance from day one.

Deliver local robustness

Pressure-test features in real-world conditions to ensure reliability across markets and edge cases.

Segment more deeply

Tailor AI behaviour by region, regulation, and user expectations for safer rollouts.

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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A major AI lab scaling to billions of users

A consumer application with AI support

A creative moment in a social app

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.

Safety

Robustness

Hallucination risk surfaced

Cultural sensitivities identified

Offensive outputs flagged

Diverse user behaviours explored

Brand reputation protected

Regulatory exposure reduced

Global rollout de-risked

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.

Enterprise brand protected

Customer trust preserved

Global consistency ensured

Misuse risk reduced

Adoption barriers lowered

Scale readiness improved

Client confidence strengthened

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.

Cultural missteps avoided

Public backlash mitigated

Brand reputation protected

Market launch de-risked

User trust strengthened

Global rollout validated

Scalable expansion enabled

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.

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Drive global market share Fine-tune your AI Show success in specific markets Defensible competitive advantage Deliver better products Build structural advantage Deliver deeper personalization

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.

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Go-to-market quickly Rapid scenario builds Evaluate a new feature Get local market feedback Accelerate time to market Pressure-test outputs Identify edge cases Reduce hallucination

Read: what's the problem with the way AI is released right now?

Curious about how businesses are leveraging the crowd? Our Global GenAI lead describes in detail how he's seeing Product Leaders adapt the crowd in this blog.

Drive deeper engagement
Drive better user adoption
Improve perceived agent success
De-risk against cultural issues

Read the blog

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

Partner with us

Route best-in-class AI evaluation techniques to a domain-specific audience

Human-in-the-Loop Refinement

Reinforcement Learning from Human Feedback

Preference Ranking

Prompt Evaluation

Safety Review

Bias Detection

Cultural Validation

Adversarial Exploration

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.