How to build great AI products with Vanta Software Developer Noam Rubin
Dec 18, 2024
auto_awesome
Noam Rubin, a Software Developer at Vanta, specializes in creating AI tools for security and compliance. He shares fascinating insights into using data-driven strategies for building AI features. Discover how customer feedback and rapid prototyping are vital in transforming ideas into practical tools. Noam explains the importance of integrating AI into compliance workflows, maintaining product quality through iterative testing, and educating engineers to democratize AI. His experiences highlight the balance between innovation, user empathy, and technical accuracy.
Vanta's AI product development thrives on customer feedback, rapid prototyping, and an iterative process that enhances efficiency and user satisfaction.
Collaboration among engineering, customer support, and continuous quality assurance through feedback loops ensures high accuracy and relevance in Vanta's AI offerings.
Deep dives
Overview of AI Products at Vanta
Vanta has developed various AI products that effectively streamline complex processes related to security and compliance. One notable product automates the response to security questionnaires, significantly reducing the workload for organizations. In addition, Vanta offers an AI tool designed for third-party risk management, which helps users analyze vast amounts of vendor documentation efficiently. These innovations reflect how Vanta leverages large language models to address specific, high-demand areas within their operational domain.
Process of Building AI Features
The development of AI features at Vanta involves a combination of customer feedback and organic idea generation. The team prioritizes understanding customer pain points and identifying integration opportunities for large language models (LLMs) where they can add value. For instance, the development of their vendor risk management tool was driven by customer needs and supported by technology advancements, leading to rapid prototyping and deployment. This iterative approach to product development ensures that the features not only meet user expectations but also enhance operational efficiency.
Importance of Collaboration and Communication
Successful AI feature development at Vanta hinges on effective collaboration among various teams within the organization. The close relationship between engineering and customer support fosters ongoing dialogue about customer challenges, enabling insights that drive innovation. Furthermore, establishing open communication channels encourages team members to share ideas and seek opportunities for AI implementation across different product lines. This culture of collaboration and transparency aligns the product team's objectives closely with customer needs, resulting in enhanced products that provide real value.
Quality Assurance and Iterative Improvement
Quality assurance for AI products at Vanta is achieved through a meticulous iterative process that relies heavily on customer feedback and data analysis. The team emphasizes the importance of maintaining a high standard of correctness, as even minor inaccuracies in security applications can have significant consequences. To this end, Vanta conducts extensive testing and continuously refines their models based on real-world usage and feedback from alpha and beta testers. This commitment to quality and improvement distinguishes Vanta’s offerings in a rapidly evolving technological landscape.
In this episode, Noam Rubin, a Software Developer at Vanta reveals how his team uses data-driven strategies to design, test, and improve cutting-edge AI features. Learn how customer insights, rapid prototyping, and iterative development transform raw ideas into tools that make compliance and security easier for businesses everywhere.
Chapters:
00:00 - Introduction 02:47 - The process of building AI products at Vanta 04:51 - The role of customer feedback in product development 06:59 - Integrating AI into security and compliance workflows 08:06 - Using data specifications to guide product development 10:10 - Collaborating with subject matter experts to refine AI models 12:14 - Iterative testing and refining AI features 14:10 - Quality control and ensuring AI accuracy 16:00 - The importance of dogfooding and internal feedback loops 18:23 - Scaling AI features and rolling them out to wider audiences 20:50 - Educating engineers and democratizing AI at Vanta 22:20 - Key lessons learned from building AI products 24:12 - Maintaining AI quality through continuous feedback 26:00 - The future of AI in business and product development
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode