Building enterprise AI: A guide to working with human experts

Building enterprise AI: A guide to working with human experts

AI models are passing new benchmarks every day. The performance gap between them is shrinking in weeks, not months. But when these models are deployed for financial analysis, legal document review, or multilingual customer support, they often fail in ways that generic evaluation data never prepared them for.

Models trained on internet data might know everything that’s publicly available, but they frequently hallucinate, confidently generating inaccurate information. It’s a growing concern highlighted in a recent MIT technology review piece exploring how AI-generated errors are even impacting courtrooms. Moreover, these models lack access to subjectivity, cultural context, and industry-specific nuance – the kind of knowledge that rarely exists online and often only comes from verified professionals.

While you might struggle to access human expertise to scale your AI training and evaluation cycles, leading enterprise AI teams use Prolific Domain Experts to get expert-validated training data in hours.

Here's how they're doing it.

When enterprise AI needs human experts

AI models trained on internet data excel at general tasks but struggle when specialized knowledge becomes central to performance. You need human experts when:

Your AI operates in regulated industries

In sectors like finance, healthcare, and law, the challenge is the complexity of interpreting documentations. Errors are costly, and judgement is often nuanced. 

• Financial AI needs CFAs who understand the implications of regulatory shifts.
  
   
• Healthcare AI relies on medical professionals to validate clinical reasoning, not just medical facts.
  
   
• Legal AI benefits from practicing attorneys who can apply current case law and navigate situations where even experts may disagree.

Cultural context matters

Multilingual applications need native speakers who understand regional business practices beyond literal translation. Global customer service AI requires cultural expertise that automated systems often miss.

Domain-specific accuracy is non-negotiable

Construction permit classification, medical device evaluation, or financial risk assessment can't rely on crowdsourced guesswork. You need professionals who understand the stakes.

Edge cases are costly

When your AI encounters scenarios outside its training distribution, expert validation prevents expensive errors and maintains user trust.

The challenge isn't finding human input – that's relatively straightforward. The difficulty lies in accessing the right experts, at scale, without slowing your development cycles

Why current approaches hold back AI teams

To scale AI training and evaluation, enterprise teams often turn to a mix of strategies. But these approaches rarely hold up under pressure. What starts as a practical solution can quickly become a source of fragility, leaving teams patching holes instead of building momentum.

Synthetic data and LLM-as-a-judge setups

These methods offer speed and cost advantages, but they often fall apart when models are forced to handle unfamiliar, high-stakes situations that require human judgement.

Traditional annotation platforms

While reliable for general labelling tasks, these platforms often rely on annotation teams without proven domain expertise. Getting started can be a slow process, and the cost structure makes them hard to justify for teams working at speed.

Generic crowdsourcing platforms

These can provide access to large pools of annotators, but lack the tooling needed for AI-specific workflows. Domain expertise may be unverified, and quality control is inconsistent, particularly for complex, high-stakes tasks.

Internal subject-matter experts

Relying on in-house professionals can yield high-quality results when internal context matters. But it’s rarely scalable. You risk pulling critical team members away from core responsibilities, slowing development cycles and limiting output.

Each of these approaches has its place. None, however, offer the combination of domain expertise, scalability, and speed required for enterprise-grade AI development.

How Prolific supports enterprise AI development

Prolific helps AI teams build faster by connecting them directly to experts whose knowledge matches their precise needs. Using this targeted approach removes guesswork and ensures your project maintains momentum.

Access to verified expert pools

With Prolific, you’re matched to professionals selected specifically for your task. Each expert is thoroughly vetted, so their knowledge aligns exactly with what your AI project requires. You can also continuously track their performance, keeping data quality consistently high.

Easy data collection workflows

Prolific integrates with your existing systems, either through simple URL linking or direct API connections. If structured annotation is required, AI Task Builder provides specialized workflows that speed up project turnaround without sacrificing accuracy.

Built for enterprise flexibility

Prolific is designed for projects that evolve quickly. You control participant selection and easily adjust workflows as your requirements shift. Detailed demographic and annotation data remain transparent, so you always have visibility into your process.

Access to verified expert pools

Instead of hoping generic annotators understand your domain, get direct access to professionals with verified credentials:

• Financial AI: Chartered Financial Analysts (CFAs) and finance professionals handle investment analysis, risk assessment, and regulatory compliance validation
• Legal AI: Practicing attorneys and legal experts manage document review, case law analysis, and jurisdiction-specific requirements
• Healthcare AI: Medical professionals validate clinical reasoning, ensure safety protocols, and handle patient data responsibly
• Multilingual AI: Native speakers with cultural expertise provide context that goes far beyond translation accuracy
• Industry specialists: Domain experts across construction, logistics, insurance, and other verticals who understand sector-specific terminology and edge cases

Professional credential verification means your data is always handled by the right experts. You can set limits on how many tasks each participant completes (they can take up to your specified limit in one session), and we track performance over time.

Specialized participant management offers quick expert recruitment for new domains or requirements, with all annotation data and demographic profiles downloadable separately for analysis.

These additions show the specific workflow and technical capabilities that enterprise teams care about. It makes the solution more concrete and actionable.

Enterprise-grade infrastructure

Managing expert validation at scale requires sophisticated workflow capabilities:

• API-ready integration connects with your existing development tools and processes. Batch processing handles large datasets efficiently. Real-time quality monitoring ] ensures consistency across complex annotation tasks.
• Flexible task configuration supports multiple input types and complex annotation schemas. Whether you're validating model outputs, creating training data, or testing edge cases, the Prolific adapts to your requirements.
• Detailed performance reporting provides transparency into who's annotating your data and how they're performing. Track accuracy, speed, and consistency metrics to continuously improve your human-AI collaboration processes.
• Built-in authenticity checks detect AI-generated responses with 98.7% accuracy and just 0.6% false positives, guaranteeing genuine human feedback. The system monitors behavioral patterns in real-time, automatically flagging suspicious responses so you can focus on high-quality data.

Case study: When domain expertise drives breakthrough performance

Shovels is an intelligence platform for the construction industry. It needed to classify building permits across 30 categories, from new construction to solar installations. The challenge involved obtaining permit data from over 20,000 jurisdictions, each using different systems and terminology.

Initially, Shovels tried a standard annotation platform. "The results were poor, and I think the majority of responses were bot-generated," explains Petra Kopić, a data engineer at Shovels.

Using Prolific's specialist recruitment, Shovels found construction industry professionals with proven track records, such as participants with more than 100 completed tasks and 98%+ approval rates. These domain experts could properly interpret permit language and classify complex building projects accurately.

Shovels now effectively classifies hundreds of millions of permits, enabling climate tech clients to identify homeowners likely to purchase building electrification equipment and connect them with qualified contractors.

What started as a data quality problem became a competitive advantage. Expert validation didn't just improve model accuracy. It enabled entirely new product capabilities that generic annotation couldn't support.

The enterprise AI playbook: Working with Domain Experts

Enterprise AI teams that successfully integrate human expertise follow a structured approach that balances speed, quality, and scale. Rather than treating expert consultation as an afterthought, leading organizations are building human intelligence directly into their AI development workflows. Here are some best practices to get you started.

Define your expert requirements upfront

Domain expertise isn’t one-size-fits-all. Even two finance graduates can follow completely different career paths, each picking up distinct skills and perspectives along the way. Knowing exactly which type of expertise fits your situation makes all the difference.

• Specify the exact expertise needed ("CPA with 5+ years in tax law," not "accountant")
  
   
• Document compliance requirements and acceptable error rates
  
   
• Set measurable quality targets based on the evaluation or training task you’re designing (e.g., 80%+ inter-rater agreement)

Pro tip: It’s worth revisiting and refining your expert profiles as projects evolve. Clearly defined expertise ensures you consistently get valuable insights.

Design your tasks well

AI training and evaluation tasks can be demanding, particularly when they require domain expertise input. A single response from the expert could involve plenty of thinking and reasoning on their end. Well-designed tasks make it easier for the expert to provide valuable feedback and data to AI systems at the quality you need.

• Break work into smaller tasks whenever possible, ideally 30 minutes or less. For longer tasks, keep requirements realistic.
  
   
• Provide clear instructions before the task. Use bullet points rather than walls of text, include visual examples of good and bad annotations, and supplement with images or videos where helpful.
  
   
• Include guidance on edge cases upfront.

Pro tip: Survey experts after your first batch, as their feedback often reveals simple improvements that can improve tasks and boost your data quality dramatically.

Start small, and then scale

It’s a good idea to start small first before scaling your tasks to more experts and data points.

• Pilot with 5–10 experts first
  
   
• Measure time-per-task and quality
  
   
• Create reusable templates for common validations
  
   

Pro tip: Build a trusted pool of experts who deliver reliable results. Using familiar experts across projects gives you more consistent data quality and predictable turnaround times, helping you streamline development and get products out faster.

Multi-layered quality checks

Not all tasks are going to be the same. It’s a good idea to map out how you measure and optimize for data-quality. A few good tips to remember:

• Use a single expert for simple tasks, but more (ideally 3+) for critical or subjective evaluations.
  
   
• Capture reasoning, not just labels ("Why did you choose this?").
  
   
• Use expert disagreements to identify edge cases.
  
   
• Share feedback with experts on how they can improve their evaluations, enhancing submission quality over time.

Pro tip: Regular feedback loops with your experts encourage continuous improvement, helping maintain consistently high-quality data as your project evolves.

The competitive advantage of expert-driven AI

Prolific delivers the combination of verified expertise and enterprise infrastructure that creates measurable competitive advantages:

• Faster time-to-market through streamlined expert feedback loops and reduced iteration cycles. While competitors spend weeks with generic annotation, you deploy improved models in days.
• Higher reliability from training data that captures real-world nuances and edge cases. Your AI handles scenarios that might break other AI systems, giving you a clear competitive advantage.
• Better regulatory compliance through expert validation of domain-specific requirements. Reduce regulatory risk while maintaining development speed.
• Improved user experience from AI systems that understand context and cultural nuances. Users trust AI that demonstrates expert-level understanding.

Accelerate your AI development

The future of enterprise AI belongs to teams that can access and integrate human expertise efficiently enough to maintain competitive development cycles while delivering the reliability your applications demand.

Move beyond generic crowdsourcing toward verified Domain Experts, beyond one-off consultations toward integrated workflows, and beyond hoping models will figure things out toward actively teaching them what they need to know.

Prolific connects enterprise AI teams directly with the right domain experts, making it easy to quickly gather the data you need.

You’ll always see clearly who's contributing to your data. Prolific fits easily into your existing systems and adapts whenever your requirements change.

Find out how enterprise AI teams have accelerated their development cycles with Prolific. 

Schedule a demo to see the difference expert-driven data makes.