How Pearl Works with AI Labs and Research Teams to Build Trusted AI Data
- marcuspark
- 3 days ago
- 11 min read
Executive Summary
Pearl AI stands at the intersection of generative AI and professional services, where accuracy is mandatory and trust defines adoption. As the AI industry experiences rapid growth and increasing regulatory focus, this piece details how Pearl partners with AI research teams—including AI researchers who drive innovation and model development—generative AI vendors, and applied AI companies to deliver expert-verified AI Data that eliminates hallucinations and accelerates enterprise rollout. It explains what these research groups are, why hallucinations pose unacceptable risks in regulated sectors, and how Pearl’s unique scale, quality, and rights-clean dataset meets that challenge.
Table of Contents
Executive Summary
Introduction: Cracks in the AI Foundation
Before We Dive In: What Do We Mean by “AI research teams”?
The Problem: Hallucinations in Professional Services
The Partnership: Pearl + AI research teams
AI Data: Pearl’s Differentiator
Regulation: Why AI Data Matters More Than Ever
The Impact on Professional Services
Conclusion: From Hallucinations to Trust
Introduction: Cracks in the Generative AI Foundation
Artificial intelligence often feels like it’s racing ahead without guardrails. Breakthroughs in generative AI dazzle the world, yet they also expose fault lines beneath the surface. Chief among them is the issue of hallucinations: moments when a system confidently generates something false. In casual use this might be quirky; in professional services, when an AI fabricates a legal citation, invents a medical treatment, or misleads a financial advisor, the risks escalate quickly from inconvenience to danger. These are clear examples of AI risks that must be carefully managed, especially in regulated sectors.
Imagine the fallout: a lawyer citing a case that doesn’t exist, a doctor relying on a hallucinated diagnosis, a financial advisor misled on compliance. These consequences show why, in industries like law, healthcare, finance, and aviation, accuracy isn’t optional. AI errors can have significant real-world impacts. Mistakes don’t just frustrate customers, they create compliance risks, legal liability, or even physical harm. That’s why the professional services sector is increasingly cautious about deploying AI. While many AI systems pose minimal risk, those used in regulated industries require much higher standards of accuracy and transparency.
Pearl AI bridges that gap. By combining generative AI with a global network of over 12,000 qualified experts across 700+ specialties, Pearl delivers AI Data that is both fast and trustworthy. And when Pearl works hand-in-hand with AI research teams, the groups training frontier models, hallucinations decrease, adoption accelerates, and enterprises trust AI in mission-critical environments.
Recent evaluations show the issue isn’t going away: according to Mashable, OpenAI’s latest o3 and o4-mini models hallucinate at higher rates than their predecessors. Even with billions invested in safety, accuracy remains elusive.
What Do We Mean by “AI research teams”?
Before we go too far, let’s clarify a term that’s easy to gloss over: AI research teams.
In this context, we don’t mean a single company or brand. We’re talking about the R&D arms of the organizations that build and refine AI models. These teams are made up of dedicated ai researchers who drive progress in the field. We’re also including independent research groups that push the boundaries of generative AI.
Think of AI research teams as the workshops where AI engines are built and tuned. Engineers, data scientists, and ai researchers experiment with training runs, fine-tuning, red-teaming, and evaluation frameworks, employing a range of ai techniques such as neural networks, optimization, and formal logic. They make models faster, more capable, and safer.
Pearl provides the clean fuel for those workshops. In practice, that means working with three key groups: the so-called ‘Magnificent 7’ companies leading the AI race, top generative AI foundational model vendors shaping frontier capabilities, and custom enterprise AI companies building tailored systems for industry-specific use. Instead of scraping the web or relying on synthetic data, Pearl supplies expert-verified, rights-clean, domain-specific AI Data that helps research teams reduce hallucinations and align models with professional standards.
What Are AI research teams? AI research teams are the R&D groups inside enterprises, startups, and research organizations that build and refine frontier AI models. Pearl fuels their work with clean, expert-verified data. The efforts of these teams are central to ongoing ai innovation that drives technological advancement and competitiveness.
Hallucinations in Professional Services
Generative AI has already proven its creative power. But creativity is precisely the problem in professional domains. A model that invents answers when it doesn’t know the truth might be fun in casual use. But in legal, medical, or financial contexts, it’s unacceptable.
In law: An AI that fabricates precedents or misquotes statutes exposes attorneys to malpractice. Automated decision making technology is increasingly used in legal processes, raising the stakes of such errors.
In healthcare: A hallucinated diagnosis endangers patients and violates compliance frameworks like HIPAA. Automated decision making technology is now widely adopted in healthcare for diagnostics and treatment recommendations.
In finance: Incorrect regulatory advice leads to fines or lost licenses. Automated decision making technology is transforming financial services, but also introduces new risks.
In aviation: A wrong answer to an immigration query or maintenance protocol creates serious safety risks. Automated decision making technology is being deployed in aviation operations, making accuracy critical.
The costs are regulatory and financial, not just reputational. Professional services demand AI that doesn’t hallucinate. AI errors in automated decision making can have serious implications, such as affecting credit scores, public reputation, or spreading misinformation. Recent survey data reinforces this: 38% of companies say AI struggles with unusual or complex cases, 26% report it gives wrong or confusing answers, and another 38% cite legal or security concerns that make it hard to use. Source: Pearl 2025 Survey: Demand for AI Acceleration Products.
These pain points highlight the urgency of expert-verified data as the missing link to unlock safe adoption. Reliable AI tools are needed to address these challenges and ensure trustworthy outcomes.
The Partnership: Pearl + AI research teams
Here’s where Pearl and leading AI research teams come together. AI research teams bring the core capabilities: computing power, model architectures, ai algorithms, training runs, optimization techniques. Pearl brings something just as important: expert-verified data that grounds those models in truth.
The collaboration strengthens the AI lifecycle:
Training: Pearl licenses its vast dataset of 30M+ expert Q&As (7.5B words across 150+ fields) to vendors and research teams. Think of it as handing model builders a law library, a medical textbook collection, and a mechanic’s repair manual rolled into one but kept continuously up-to-date and verified by real professionals.
Fine-tuning: Pearl creates custom datasets to sharpen performance on edge cases in law, medicine, finance, and beyond. These aren’t just standard examples; they’re the thorny, nuanced problems that real professionals grapple with. The ones most likely to trip up a general-purpose model.
Evaluation (Evals): Pearl’s network of doctors, lawyers, mechanics, and other specialists provide real-time scoring and feedback on model outputs. Instead of abstract benchmarks, this means a physician evaluating whether a triage suggestion makes clinical sense, or a lawyer checking that a citation is authentic.
Red-teaming: Experts stress-test models with adversarial prompts to uncover vulnerabilities before launch. This is the equivalent of a dress rehearsal under pressure, surfacing weaknesses before customers or regulators do.
Reinforcement Learning with Human Feedback (RLHF): Pearl’s Experts select preferred responses, aligning AI with human judgment. This isn’t just about preference; it’s about anchoring AI in professional standards so outputs aren’t merely plausible, but trustworthy.
According to the Pearl 2025 Survey: Demand for AI Acceleration Products, 42% of executives believe combining AI with human experts ensures professional, high-quality responses, and among business leaders that number rises to 66%. This partnership ensures that research teams don’t work in isolation. They gain access to a living, breathing knowledge network of humans who validate, correct, and refine. Many of the methods used by research teams are powered by machine learning, which enables continuous improvement and innovation in AI systems.
Pearl + AI research teams Lifecycle Impact: Training • Fine-tuning • Evals • Red-teaming • RLHF — Pearl’s Experts strengthen research teams at every stage.
AI Training Data: Pearl’s Differentiator
Here’s the big picture of our dataset: We’ve got more than 30 million expert Q&A conversations, or about 7.5 billion words spanning over 150 fields.
Scale & Uniqueness: Our dataset spans legal, medical, veterinary, home repair, tech support, and 150+ other domains where LLMs often struggle. On average, each interaction has ~10 back-and-forth exchanges between a credentialed expert and a consumer.
Languages & Geography: Primarily English (96%), with meaningful coverage in Spanish, German,French, and Japanese, spanning 196 countries. U.S. represents ~77% of questions; UK, Canada,Australia, and others make up the remainder.
Clean Rights: Built organically over 2 decades at significant cost, our rights are clean and defensible, unlike much of the data currently in circulation.
Unpublished Data: Roughly 20% of our content has never been published or crawled (e.g.,unanswered questions or low-rated answers). This “dark data” may be even more valuable, as it exposes exactly the kinds of complex queries that stump both humans and machines.
Growth & Refresh: The dataset grows by ~5M new expert Q&A interactions per year, and we canprovide quarterly or semi-annual refreshes.
Supplemental Data: We also have 1B+ Pearl chatbot conversations (not included in the above totals) that can be made available to add scale and diversity.
Pearl’s AI Data stands apart in two distinct ways: through data licensing and through data production.
Pearl’s data is sourced from a vast network of professionals, ensuring that the information is both comprehensive and expert-verified. This data is used by AI systems to identify patterns, improve diagnostic accuracy, and enhance overall performance. Both traditional machine learning and deep learning models benefit from Pearl’s high-quality datasets, which provide the foundation for robust, reliable, and scalable AI solutions in dentistry.
By providing expert-verified data, Pearl enables the development of new AI tools for professional services, driving innovation and improving outcomes across the dental industry.
Data Licensing Advantage
Pearl licenses its vast dataset of over 30M+ expert Q&As and billions of words across 150+ fields. This licensed data is enterprise-ready, rights-clean, and grounded in professional expertise. It is immediately usable by research teams and vendors—including those developing large language models and generative AI systems—to train, fine-tune, and evaluate their models.
Data Production Advantage
Pearl continuously produces new, high-quality data. With more than 5M fresh expert conversations added annually, every data point is anchored in a rigorous verification process that includes multiple layers of quality control. Ongoing quality controls ensure reliability, including credential checks, peer review, and customer ratings. Unlike web-scraped content, this includes 20% unpublished material and niche domain coverage that others cannot access. Pearl’s data covers both real and virtual environments relevant to professional services, ensuring comprehensive and applicable datasets.
Together, these two streams, licensing of a massive, rights-clean dataset and ongoing production of expert-verified data make Pearl’s AI Data a category of its own: expert-verified, rights-clean, and enterprise-ready.
Pearl also goes beyond the basics with second-level expertise and handling of AI stumps which are the highest-demand services in this space. Second-level expertise means that when an AI output is ambiguous or partial, Pearl routes it to a domain expert with deeper specialization for resolution. AI stumps are those hard, unsolved questions that standard models fail on; Pearl’s expert network supplies authoritative answers, creating new, high-value training data where the gaps are largest.
AI Data at a Glance: 30M+ Q&As • 7.5B words • 1B+ chatbot convos • 20% unpublished • 12,000+ experts verifying quality.
Regulation: Why AI Data Matters More Than Ever for High Risk AI Systems
The regulatory environment is shifting quickly. Just this year, California passed SB 53, the landmark AI transparency bill. It requires large AI developers to publish safety frameworks, report incidents, and protect whistleblowers. The message is clear: transparency and safety are no longer optional. This is part of a broader trend in ai regulations and ai regulation, as both the US and other countries move to formalize oversight of artificial intelligence.
Across the Atlantic, the EU AI Act created a tiered system that enforces stricter oversight of high-risk AI applications — exactly the kinds used in law, healthcare, and finance. The EU AI Act stands as a leading example of regulation of AI and ai legislation in the European Union, setting a global benchmark for comprehensive regulatory frameworks.
In the US, NIST’s AI Risk Management Framework sets voluntary standards that are quickly becoming de facto expectations. However, there is currently no comprehensive ai specific legislation or federal laws dedicated solely to AI, highlighting the evolving role of the federal government and the ongoing development of a national ai strategy. President Biden's executive order on AI has further shaped US policy, emphasizing the importance of trustworthy ai, responsible ai innovation, and ai governance.
Globally, regulatory frameworks vary widely. The European Union leads with its artificial intelligence act, while the UK government relies on sector-specific rules and principles-based approaches. China has introduced targeted regulations for generative ai services, supporting domestic ai development and balancing innovation with oversight. Many countries are adopting ai action plan initiatives and regulatory sandboxes aimed at encouraging ai innovation.
Compliance now requires a focus on risk assessment and the identification of high risk ai systems under these frameworks. There is a growing emphasis on trustworthy ai systems and trustworthy ai in international standards, with the oecd ai principles serving as a foundation for international cooperation. Regulating the use of ai is central to ensuring responsible ai innovation and protecting fundamental rights.
In this environment, Pearl’s AI Data is more than a technical advantage. It is a compliance enabler. By grounding models in expert-verified truth, Pearl helps vendors and research teams meet the safety, transparency, and accountability demands regulators are pushing.
Clean rights — built over 20 years, legally solid and defensible.
That isn’t just a line, it’s a direct answer to what regulators are asking for.
Regulatory Proof Point: Laws like California’s SB 53 and the EU AI Act demand safety, transparency, and accountability. Pearl’s rights-clean, expert-verified data delivers exactly that.
AI safety remains central to all these regulatory efforts, ensuring that innovation proceeds with appropriate safeguards and public trust.
The Impact on Professional Services
Risk Mitigation
AI helps professional services firms identify and mitigate risks more effectively, such as detecting fraud or ensuring compliance with regulations. The importance of risk assessment in identifying and managing potential issues is central to effective risk mitigation, especially within governance and accountability frameworks for regulatory standards. Deploying reliable AI systems is crucial in these industries to maintain trust, ensure accuracy, and uphold ethical standards.
Efficiency Gains
Automation of routine tasks frees professionals to focus on higher-value work, increasing productivity and reducing costs.
Better Service Delivery
AI enables more personalized and timely services for clients, improving satisfaction and outcomes. Advanced AI technology plays a significant role in enhancing the quality and effectiveness of service delivery. Virtual assistants, powered by artificial intelligence, provide 24/7 client support and handle inquiries efficiently. Additionally, natural language processing allows these systems to communicate more effectively and retrieve information accurately, further improving client interactions.
Compliance and Risk Mitigation
With regulators tightening the reins and a growing focus on AI safety, firms can’t risk deploying models that hallucinate. AI errors are a key concern, as they can lead to unintended impacts and regulatory scrutiny. Pearl’s AI Data reduces that risk, aligning AI with both ethical guidelines and legal requirements.
Faster Adoption
Trust is the currency of adoption. By reducing hallucinations, Pearl accelerates stakeholder confidence. That means AI initiatives don’t get stuck in pilot projects — they move into production.
Better Service Delivery
For law firms, it means faster contract reviews with fewer errors. For hospitals, it means safer AI-assisted triage. For financial advisors, it means more accurate compliance checks. For airports, it means reliable information for staff and travelers. In all these industries, ai agents—autonomous software entities capable of perceiving their environment, making decisions, and executing actions independently—support professionals by automating complex tasks and improving efficiency.
Enterprise Readiness at Scale
Pearl isn’t a niche player. With 43M+ monthly visitors, 220k daily AI chats, and 12k daily expert answers, the scale is already demonstrated. Enterprises don't just get data, they get data at enterprise scale, validated in the real world.
Trusted at Scale: 43M+ monthly visitors • 220k daily AI chats • 12k daily expert answers — Pearl’s data is proven in the real world.
Conclusion: From Hallucinations to Trust
Work with us. Pearl AI offers 12,000 licensed experts and PhDs with certifications and years of experience available 24/7 to deliver post-training AI data production for your AI research team. We also have 30M historical questions already licensed and generate millions of new, incremental data points every year. Partnering with Pearl gives you access to expert-verified, rights-clean, enterprise-ready data that fuels your models with accuracy and confidence.
Now is the time to act. Partner with Pearl to make your models safer, compliant, and enterprise-ready.