I build software for places where the customer is talking, but the person who can fix the problem is too far away to hear it.
I co-founded ResiDesk because renting is the biggest thing most people buy every month, and the experience is still weirdly bad. Residents say what is broken every day. Owners and operators need that truth in a form they can act on.
Before that, I built outcome-based lending at Climb Credit and advisor-facing analytics at BlackRock. I came out of applied physics at Cornell with a simple bias: give me the context, the measurement, and the actual job.
The throughline is not "AI for real estate." It is customer truth, operational follow-through, and tools that people still use after the demo is over.
Most of my attention is on ResiDesk. The product texts with residents, understands property context, helps the team answer well, and gives owners a clearer picture of what people actually want from the building.
Looking for
Owners and operators who want the truth.
The best conversations start with teams who know their residents are already telling them something important, but cannot afford to read every text, review, ticket, and survey by hand.
Writing about
What happens after the answer.
AI is interesting when it changes the next task: who handles it, what they know, how fast they can move, and whether the resident has to explain the same problem again.
Not useful
AI that only talks.
If there is no workflow owner, no source of truth, and no real decision afterward, it is probably theater.
This is the clearest version of what shows up across the talks: talk to customers, make the work visible, use AI to remove drag, and do not confuse a good demo with a product people trust.
01 / Customer
Talk to the customer.
That is still business 101. In housing, the hard part is doing it at scale and getting what residents say back to the people who can change the building.
02 / Context
Show the actual job.
I do not think well in abstractions for their own sake. Give me the workflow, the stakes, the strange edge cases, and the person who owns the outcome.
03 / Adoption
Demos are not adoption.
I learned this early at BlackRock. A prototype can win the room and still lose to the spreadsheet the next morning.
04 / AI
Shorten the commute.
I do not need AI to do the whole job. I need it to move a task from stuck to nearly finished, while the person still owns the judgment.
05 / Trust
Keep people where trust matters.
Residents trust the product because there is a human team behind it. The AI should make that team faster, better informed, and less buried.
06 / Team
Hire people who can carry context.
The best builders can take in a messy situation, find the few facts that matter, and move without waiting for a narrow script.
Story map
[MODULE 05]
From resident complaint to better building
[STEP 01 / LISTEN]
Let residents say what is actually happening.
A useful system starts with the ordinary stuff: the broken washer, the pet policy question, the Wi-Fi complaint, the package-room mess, the reason someone may not renew.
I grew up around research, so the default future looked academic. I studied applied physics at Cornell because it sat between the things I liked most: fundamental science, real systems, and problems where small details changed the outcome.
Software came in sideways. I was in an electron microscopy lab and wrote code to make a magnetic-noise setup faster. It saved hours of manual work immediately, which made the lesson hard to ignore: the right tool changes the shape of the job.
The industries changed. The job did not. At BlackRock it meant making institutional tools usable for advisors. At Climb Credit it meant underwriting against outcomes. At ResiDesk it means helping housing companies hear residents clearly enough to make better calls.
Physics
Software became the lever.
I wrote a tool in an electron microscopy lab to speed up magnetic-noise setup. It saved enough time, fast enough, that software stopped feeling abstract.
BlackRock
Real stakes sharpen the interface.
I came back six months later, re-interviewed, and moved to New York. It was where I learned how much interface quality matters when a user is making decisions with real money behind them.
Climb Credit
Outcomes changed the product.
Instead of asking who looked safest on paper, we asked what happened to earnings after the program. That reframed underwriting, product, and data, and took annual loan volume from $1 million to $300 million.
ResiDesk
Housing should know its customer.
Residents tell buildings what is working and what is not every day. The work is making that legible to the owner, useful to the operator, and better for the resident.
The work has mostly been the same job in different settings: find the customer truth inside a messy process, then build the shortest responsible path from context to action.
Resident experience
7%
Reported renewal and rent lift after resident feedback moved earlier into the operating cadence.
Climb Credit
$1M → $300M
Annual loan volume growth after treating student outcomes as product data, not marketing decoration.
Advisor workflow
$40M ARR
Advisor-facing analytics product taken from zero to $40 million ARR in year one.
We help rental-property owners and operators understand what residents are asking for across renewals, rent, maintenance, and staffing. The product earns its keep when it changes the next action: owner, urgency, context, and follow-through.
We underwrote against a different question: not who looked safest on paper, but what happened to a graduate's earnings. That forced outcomes into the product's infrastructure.
The job was turning institutional infrastructure into something advisors could use with clients. Same information underneath, but packaged for the moment where a person had to explain, compare, and decide.
These are the best way to hear the argument directly: what I learned from physics, why demos can mislead, how ResiDesk works, and where AI actually earns its place.
I write when the consensus feels too smooth. Most pieces circle back to AI, product adoption, and the gap between a model that can answer and a system a team keeps using at work.
If a tool does not help someone finish a real task sooner, with less context loss, it is decoration.
Understand the job first.
Without knowing what someone is actually trying to do, you are just rearranging the screen.
Build the harness, not just the model.
The model is one part. Context, tools, guardrails, evaluation, and the handoff into someone's day decide whether it matters.
Demos lie by omission.
What matters is whether people still reach for it mid-work, mid-mess, with no audience and no demo to grade.
FAQ
[MODULE 11]
Questions that come up a lot
What AI systems do I actually build?
I build AI that fits into how people already work. At ResiDesk, that means helping property teams answer residents, understand what is really happening in the building, and get the right work to the right person.
What have I built before ResiDesk?
I worked at Climb Credit and BlackRock. At Climb, I helped grow annual loan volume from $1 million to $300 million. At BlackRock, I built a retail analytics product that reached $40 million ARR in its first year.
What do I usually write and speak about?
How AI products hold up in practice: agent workflows, LLM evaluation, product loops, and what separates a strong demo from something that works on a Tuesday afternoon. Recent talks start with residents and end with the operating system around the model.
What is my view on AI?
I care less about whether something looks impressive and more about whether it helps someone make a better call. That usually means getting context right, measuring what matters, and keeping a human in the loop before automating the wrong thing.
I have invested in more than 100 startups and mentored through Techstars. I tend to back founders who are close to the problem, close to the customer, and honest about what they do not know yet.
That honesty matters more now because generic advice is free. What still counts is specific context, good judgment, and helping someone reach the next real decision faster.
