Hometap Innovation, Technology & Agility

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

AI coding tools, such as Microsoft Copilot, allow us to augment and add leverage to our development process; a data engineer noted that the adoption of AI has expedited our testing and iteration process tenfold from two years ago, prior to wider-scale large language model usage. Other engineers have seen a 10 to15 percent increase in overall productivity in their day-to-day coding. We’ve even seen instances where product managers, who are not full-time programmers, have been able to use LLM tools to create working prototypes during the software development process. For a small company like Hometap, doing more with less is critical as we continue on our growth trajectory.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

Hometap has a dedicated data organization with data scientists, data engineers and data analysts who keep the rest of the business apprised of changes in this area. This department works on critical roadmap items but is also available to consult with any part of our business that wants data-driven insights — and at Hometap, that’s just about everyone. 

The rest of our engineering team is also constantly sharing new ideas and tools, and we have strived to keep up with all of these changes by creating a focused but collegial atmosphere.

Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time to market?

Staying true to our mission of helping make homeownership less stressful and more accessible, Hometap wants to be able to say “yes” to as many homeowners as possible. Most traditional home equity products look only at one’s FICO score when evaluating creditworthiness; however, Hometap is able to use a proprietary supervised model in our underwriting process that takes a more holistic view of a homeowner’s financial situation in order to paint a more accurate picture and better inform our investment decision.

LLMs have also played a critical role in understanding trends in our business. We use LLMs to analyze all communications we send to our homeowners in order to identify patterns and needs. We’re also beginning to explore how we can use LLMs within other contexts across our business — namely, making sense of all of the data we have at our disposal to improve our operational prowess and provide homeowners with a best-in-class experience in service to our mission.