Artificial Intelligence (AI) has permeated industries, reshaping how businesses operate and innovate. However, while the spotlight often shines on cutting-edge algorithms and scalable compute resources, one crucial element tends to be overlooked: human annotation and expertise. This foundation of vertical AI agents—specialized AI systems tailored for specific industries or domains—is indispensable to their success. In this blog post, we’ll explore why human annotation and domain knowledge are vital, how they drive the efficacy and impact of vertical AI agents, and the steps necessary for creating strong, accurate AI agents for critical applications like law.
Unlike general-purpose AI systems, vertical AI agents are designed to address the unique challenges and requirements of specific sectors, such as healthcare, finance, retail, and manufacturing. These agents are built with a deep understanding of domain-specific nuances, enabling them to deliver insights and automation tailored to the context in which they operate. Vertical AI agents are at the forefront of solving niche problems that demand precise and reliable solutions.
In many ways, vertical AI agents are the future of Software as a Service (SaaS). While traditional SaaS platforms are software tools that businesses or individuals use for various tasks, vertical AI agents go beyond simply being tools [1]. They combine advanced software capabilities with human expertise, eliminating the need for manual operations such as data entry and approval by users. In essence, vertical AI agents are more than just software: they represent a partnership between AI and human knowledge, automating workflows, enhancing accuracy, and providing deeper insights, all without the need for constant human input.
The key difference between SaaS and vertical AI agents lies in this synergy of people and software. While SaaS is essentially a software product that users operate, vertical AI agents are a seamless blend of human input and AI, where the software does the heavy lifting, and human expertise ensures that results are reliable, accurate, and aligned with real-world contexts.
For example, platforms like Giga ML [2] provide AI-powered tools that can be used to streamline various machine learning workflows. Giga ML showcases how AI solutions can be integrated with human oversight to create vertical AI agents that tackle industry-specific challenges, helping businesses develop high-performing AI models for domains such as finance, healthcare, and more. Additionally, Wendy's has implemented an AI-driven voice ordering system in their drive-thrus, enhancing customer experience and operational efficiency [3]
Another prominent example is Zest AI [4], which uses AI to help lenders make more accurate decisions by analyzing loan applicants’ data. The company’s vertical AI agent automates the underwriting process while utilizing domain-specific knowledge to make decisions that better reflect the risk profile of each applicant, demonstrating the powerful combination of human and AI expertise in financial services.
Data Quality and Relevance: High-quality training data is the lifeblood of any AI system. For vertical AI agents, the need for accurate and contextually relevant data is even greater. Human annotators bring invaluable expertise to the table, ensuring that datasets are labeled with precision. This step is critical for domains where accuracy is vital to the effectiveness of the system.
Understanding Nuances: In many industries, data can have subtle variations that are difficult for machines to interpret without guidance. Human annotators with specialized knowledge are needed to properly label these data points, ensuring that the AI system can make accurate decisions based on detailed insights.
Building Trustworthy AI: Human annotation ensures that AI systems make decisions based on reliable and unbiased data. This is particularly crucial in applications like hiring or lending, where fairness and transparency are paramount.
Contextual Relevance: Domain experts help define the scope and objectives of vertical AI agents. They understand the pain points and opportunities within their fields, shaping the AI’s development to align with real-world needs. Tempus, a leader in AI-powered precision medicine, exemplifies how domain experts shape the development of vertical AI agents for healthcare. By combining AI with clinical and molecular data, Tempus is able to help physicians make better treatment decisions based on real-world, domain-specific knowledge. They have built one of the world's largest libraries of multimodal healthcare data, which includes over 250,000 patients with various types of data [5].
Validation and Feedback: Domain knowledge isn’t just important during the training phase; it’s also critical for validating AI outputs. Continuous input from experts ensures the system remains accurate and evolves with changing industry dynamics.
Enhancing Interpretability: For AI agents to gain user trust, they must provide explainable results. Domain experts play a key role in developing interpretable models that users can understand and act upon confidently.
For critical applications like law, where accuracy and reliability are paramount, it is essential to follow a robust development process to ensure the AI agents are strong, effective, and trustworthy. Below are the key steps for developing vertical AI agents in such domains, emphasizing the value of domain experts in evaluating the results:
While the value of human annotation and expertise is clear, integrating these elements into AI development comes with challenges:
However, these challenges also present opportunities. Leveraging platforms that combine human expertise with AI-assisted annotation tools can significantly accelerate the process. Additionally, fostering collaborations between academia, industry, and annotation platforms can lead to the creation of high-quality datasets at scale.
As AI continues to transform industries, the importance of human annotation and domain knowledge cannot be overstated. These elements form the backbone of vertical AI agents, ensuring they are accurate, relevant, and trustworthy. By recognizing and investing in this foundational layer, businesses and researchers can unlock the full potential of AI to tackle some of the world’s most complex challenges, especially in critical applications like law, healthcare, and finance.
By following the right steps—focusing on user needs, breaking down tasks, thoroughly testing, and continuously improving—we can create AI agents that don’t just function but excel in their specific domains, elevating industries and transforming business operations with the power of human expertise and AI working in tandem.
[1] https://zbrain.ai/vertical-ai-agents/
[2] https://gigaml.com/
[3] Fortune. (2024, October 15). Wendy’s and Google partner to use AI for drive-thru expansion. Fortune. Retrieved from https://fortune.com/2024/10/15/wendy-google-ai-drive-thru-expansion/
[4] https://www.zest.ai/
[5] Tempus. (n.d.). Q&A: Harnessing AI with multimodal data to transform drug development. Tempus. Retrieved from https://www.tempus.com/resources/content/articles/qa-harnessing-ai-with-multimodal-data-to-transform-drug-development/
[6] https://www.rossintelligence.com/what-is-ai
No matter how specific your needs, or how complex your inputs, we’re here to show you how our innovative approach to data labelling, preprocessing, and governance can unlock Perles of wisdom for companies of all shapes and sizes.