Hometap Innovation & Technology Culture

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.

What tools support your day-to-day work?

AI has become instrumental in how our team operates. I use it to draft and stress-test specs before they reach engineering, write and debug the SQL and Python I'd otherwise grind through by hand and compress a sprawling research question into something I can hand a stakeholder in minutes.

As one of the people who championed Hometap’s AI rollout, I've watched how much faster teams move once they stop treating these tools as novelties and build them into their actual workflows. For a small team the leverage compounds quickly. None of this is about replacing judgment. It's about clearing the slow, mechanical parts off your plate so your time goes to the work that actually needs a human: deciding what to build, working out what the data means and judging whether an answer holds up.

How does your team experiment?

In the open, on a cadence, biased toward shipping over theorizing. We run an AI Office Hours series built around live problems - the messy, poorly-defined ones - because that's where prompting actually moves the needle and where most people quietly give up and decide the tool doesn't work.

Office hours get people in the room, but the bigger goal is making sure what one person figures out doesn't stay locked with that one person. We run an AI Spotlight in the company newsletter that shows how real teams are using these tools, so a workflow someone cracked on the underwriting side is visible to everyone else instead of getting reinvented from scratch three teams over. We're also deliberate about one distinction that's easy to skip: a clever one-off prompt is a trick, a reusable capability is an asset and we put our energy into the second. A lot of "AI experimentation" is people poking at a chat window in isolation, relearning the same lessons in parallel. We built a framework that pools those lessons instead, so every discovery raises the floor for everyone and the company gets better as a unit.

How does your company adapt to change?

When it became clear that AI fluency was turning into a core professional skill, Hometap didn't hand people a login and wish them luck. We built an ecosystem around it.

We started with a formal AI policy, built with Legal, that set the guardrails before anyone logged in: how homeowner data is protected, what's in bounds, who's accountable. Then we rolled out Claude and Gemini company-wide, backed by required training, dedicated office hours and community channels for the day-to-day questions. None of those pieces works alone. A tool dropped on people with no system around it is a liability. The same tool inside a real system is a force multiplier.

The mindset underneath this was that AI is simply part of how work gets done now at Hometap and equipping people to use AI well is a business imperative. So we ran it as capability-building rather than a top-down mandate. The people getting real leverage out of these tools get pulled into team meetings to show what's working and we keep concrete use cases visible so the whole company climbs together. The shift wasn't adopting a tool. It was building a shared language for how we work.