Join us on this week's episode of the Slice of Finance podcast, hosted by Jared S. Taylor!

Our Guest: Mike Conover, Founder & CEO at Brightwave.

Key Highlights:

• Mike Conover’s journey: From LinkedIn and Databricks to founding Brightwave.

• Revolutionizing workflows: How AI enhances financial research efficiency.

• Breaking down complexity: Tackling challenges with prescriptive AI workflows.

• Series A success: $15M raised to scale Brightwave’s impact.

• Pragmatic AI approach: Balancing innovation with real-world application.

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The Brightwave Origin Story

Mike Conover’s journey to founding Brightwave is rooted in a career focused on deciphering complex systems. With a PhD in a field adjacent to machine learning and formative roles at LinkedIn and Databricks, Conover built a reputation for creating AI tools that reveal hidden patterns. These early experiences culminated in Brightwave, an AI-powered research platform designed to empower financial professionals.

“Brightwave was born out of my fascination with complex systems like the global economy,” Conover shared. His previous work—predicting market trends using LinkedIn data and pioneering open-source language models at Databricks—laid the foundation for tackling financial workflows with AI.

AI: Changing the Game in Financial Research

Traditionally, financial analysts were limited by the time-consuming nature of research, from combing through earnings calls to analyzing market trends. Brightwave is changing this paradigm by leveraging AI to process vast amounts of data at unprecedented speeds.

For example, Brightwave helps analysts evaluate public equity comparables or review dozens of documents for private credit deals—all within a fraction of the time. Conover explained, “It’s no longer clear that throwing more people at these problems is the best solution.”

Challenges in Building a Financial AI Platform

While early AI promises focused on fully autonomous systems, Conover quickly realized that prescriptive, guided workflows yield better results. Brightwave breaks complex research questions into actionable sub-tasks, such as analyzing semiconductor supply chains impacted by geopolitical tensions.

Instead of relying solely on large language models, Brightwave combines cutting-edge AI with classic machine learning techniques. “We’ve learned that extracting high-quality insights is technically challenging,” Conover admitted, emphasizing the importance of creative constraints in delivering practical solutions.

AI in Context: Hype vs. Reality

Despite the buzz surrounding AI, Conover is candid about its current limitations. Many demonstrations, he explained, rely on “garden path demos”—perfectly scripted scenarios that often fail in real-world applications. Brightwave’s approach prioritizes foundational problem-solving over flashy features, ensuring reliable performance even in complex scenarios like financial data extraction.

Scaling Brightwave: Series A Funding and Future Goals

In October, Brightwave announced a $15 million Series A round, led by Decibel Partners and Omers Ventures. The funds will accelerate Brightwave’s mission to transform financial research. Key initiatives include scaling the team, expanding go-to-market strategies, and building a high-resolution knowledge graph of the global economy.

“We’re hiring top-tier talent, like our front-end engineering lead who worked on Instagram’s smart glasses integration,” Conover noted. This pragmatic focus ensures that Brightwave’s tools remain at the cutting edge of usability and innovation.

Brightwave is poised to redefine how financial professionals work, enabling them to uncover insights faster and more effectively. As AI continues to evolve, Conover and his team remain committed to bridging the gap between technological potential and practical application.

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