Executive Summary

FinChat, the innovative platform transforming investment research through AI, partnered with Ribbon.ai to identify exceptional engineering talent for their growing team. With their user base expanding 10X in the past 12 months to over 300,000 users, they needed to find a "10x engineer" who could take their data infrastructure to the next level.

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Key Results

• 65% reduction in initial screening time
• Higher quality technical conversations in final interviews
• More efficient identification of candidates with both technical and financial expertise

Challenge

FinChat faced a specific hiring challenge: finding an exceptional backend engineer who could bridge the gap between cutting-edge AI technology and institutional-grade financial data. Their platform processes complex financial information from over 100,000 global companies, requiring someone who understands both the technical and financial aspects of their work.

As a lean, fast-moving team, they needed an efficient way to identify candidates who could match their high technical standards without consuming valuable engineering leadership time in initial screenings.

Solution

Using Ribbon.ai's AI interviewer, FinChat created a specialized technical screening process that evaluated candidates across crucial dimensions:

• Python expertise and data parsing capabilities
• Experience with databases and API development
• Understanding of structured and unstructured data handling
• Knowledge of AI technologies, specifically LLMs
• Financial domain knowledge and investment concepts

The AI interviewer conducted initial technical screenings, allowing FinChat's engineering leadership to focus their time on the most promising candidates.

Impact and Results

The new screening process transformed FinChat's ability to identify top engineering talent efficiently. The AI interviewer helped surface candidates who demonstrated both technical excellence and financial domain understanding, reducing the time engineering leaders spent on preliminary interviews.

Key improvements included:

• More thorough initial assessment
• Better evaluation of candidates' financial knowledge
• Faster identification of candidates ready for in-depth technical discussions

Most importantly, the process helped FinChat maintain their high technical standards while making their hiring process more efficient.

Leadership Perspective

“Ribbon is saving us an immense amount of time in the initial screening process. It allows me to screen 10x the amount of candidates that we usually would have capacity for.  When there are needles in the haystack I want to make sure I look in every haystack.Plus, candidates are able to complete it on their own time to avoid additional scheduling headaches.”
- Braden Dennis, CEO of FinChat

About FinChat

FinChat is setting a new standard for investment research by combining institutional-quality financial data with advanced AI capabilities. Their platform serves over 300,000 users and growing, providing deep insights into global public companies through AI-powered analysis. Used by leading institutions, FinChat is transforming how investors access and analyze financial information. As a venture-backed company that grew from bootstrapped beginnings, they're building the best financial data company for the AI era.

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