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The first time I encountered EliseAI, I didn’t recognize it as “AI.” I was moving apartments and messaging with what I thought was a leasing agent late at night. They replied instantly and didn’t make me wait until morning to book a tour - I thanked them and went to sleep. It wasn’t until much later that I realized I hadn’t been talking to a person at all.
A few months ago, I was researching AI companies and landed on EliseAI’s website, and I noticed something: the same building I was messaging was a customer of theirs. The experience I had written off as “unexpectedly smooth property management” now had a clear source.
A few weeks later, I attended one of EliseAI’s recruiting events; a healthcare engineering session and then an engineering happy hour at The Flying Cock. While I expected casual conversation with several engineers, I instead found myself in the middle of a thoughtful debate about how to make systems run better. People didn’t talk about the company as much as the problems they wanted to solve. I left thinking the environment matched how I like to work. That was when I realized something about the kind of AI EliseAI was building.
I didn’t want to join because they were building AI that seemed like a magic trick. I wanted to join because it didn’t feel like a trick at all.
Two weeks and several interviews later, I had an offer.
I was told this wasn’t the fastest turnaround the company has ever done.
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Day One
6:15 AM: Resetting the System
I went to the gym mostly to manage first-day adrenaline. I didn’t know what the day would look like, but “predictable” wasn’t the expectation.
8:50 AM: First Entry
New hire cohort on the second floor: engineering, sales, CS. Early bonding over the usual startup-onboarding uncertainty. Part curiosity, part “what did I get myself into.”
9:00 AM: Onboarding
We did the standard walkthrough: office layout, where engineering sits, where healthcare sits, who moves desks most often (everyone, because we’re growing so fast that the office is a giant moving puzzle).
Laptop setup, access requests, initial cultural overview.
One mantra surprised people: “praise privately, criticize publicly”. It reads counterintuitive until you see the intent: Direct feedback is normal. People unblock each other quickly. We all learn from each other's mistakes. Less about performance theater, more about shared situational awareness. This works only when ego is checked at the door and feedback stays objective, focused on outcomes and learnings rather than individuals, so sharing mistakes and fixes publicly becomes a tool for collective growth, not blame.
11:00 AM: First PM Ping
Mid-onboarding, I got a Slack DM from James, a PM:
“Want to sync today? I have an idea for your first task.”
I had received Slack access approximately five minutes earlier.
We met briefly. He walked me through the Move-In product, an end-to-end workflow covering everything from signing a lease to actually unpacking boxes. Lots of handoffs, lots of opportunities for things to fall through cracks, and many places where automation actually matters.
By the end of the conversation, I had my first assignment. Five hours into the job.
12:00 PM: Lunch + Team Intro
The company paying for meals isn’t a perk so much as a system design choice. When 95% of the company is on-site, it becomes the daily forum where engineering, ops, GTM, and product collide. It naturally creates cross-team overlap. Within a few minutes I had context on active projects, New York housing, and the small details that make a workplace feel shared - like realizing everyone around the table was wearing the same EliseAI hoodie I’d just received. These conversations are rarely about the “workday”, rather the world our products operate in and the broader context.
1:00 PM: Engineering Setup
Provisioning: the universal equalizer.
VPN, database access, local services, environment variables that appear only after their counterparts are satisfied. My team helped me work through what felt like a progressively unlocking puzzle. Standard onboarding ritual.
3:00 PM: Coffee Sync
Lena and Leo (Software Engineers working on the Maintenance product) put a coffee chat on my calendar. We stepped out to a nearby café. These conversations act as informal orientation to the unwritten parts of the culture. We talked about workflow patterns, team norms, and the parts of startup life that are both chaotic and productive at the same time.
3:30 PM: First Real Task
The assignment was a UI change to the Move-In checklist. Small, but intentionally end-to-end. Understand the user flow, modify it, test it, ship it.
The point wasn’t just the feature. It let me dive right into a real use case and start learning about our customers immediately. This is a consistent theme: tangible participation is expected from everyone, starting on day one and continuing every day thereafter.
6:00 PM- Dinner with Engineering
Ryan (VP of Engineering) added me to a group dinner order. We ended up with Mexican food and several engineers around a table discussing ongoing issues, debugging stories, and general context you don’t get in onboarding docs.
7:00–10:00 PM- Shipping
I wrapped up the UI update, resolved the final blockers, opened a PR, and got it ready to merge.
By the end of Day One, something I built was headed toward production.
10:00 PM- Debrief
I called my mom on the walk home. She was surprised something I built would be used that quickly; and it was user-facing. This is a pattern at EliseAI:
- A day feels dense because work isn’t queued behind layers of process.
- Ownership starts immediately.
- Output matters, not ceremony.
I checked on the PR one last time, then signed off.
What Day One Reveals Beneath the Surface
1. The pace is fast because decisions are close to the work.
Meetings exist only when needed. You know who the user is, what the problem is, and why the work matters. That clarity removes most delays.
2. Impact isn’t deferred.
My first feature shipped instantly. It wasn’t hidden behind internal flags - real residents interacted with it.
3. The culture is demanding and supportive at the same time.
Direct feedback is normal. People unblock each other quickly. High expectations pair with high availability.
Two Months Later
When people ask how long I’ve been here, “two and a half months” feels misleading. Two and a half months here contains the volume of half a year somewhere else.
I’ve sat in standups with Tony (CTO), shipped multiple user-facing features, and moved at a pace that feels closer to Series A/B startups than a Series E company.
What drew me in was that the work we’re doing is leading us towards a generational shift. Soon, AI won’t be a novelty layered onto software but will be the infrastructure of how real-world systems operate.
EliseAI is building for the moments where real life gets messy: tour scheduling, move-ins, maintenance, medical follow-ups. When something breaks there, people feel it immediately.
When you ship, you see the effect just as quickly. A small fix can mean fewer missed appointments or a resident getting an appliance fixed.
The work is demanding, but proportionally rewarding. You don’t have to wait months to see whether something you built mattered. You usually see it by the next morning. This is why I became an engineer: to build systems that solve practical problems, to own them end to end, and to see the results reach real users quickly. EliseAI makes that possible from day one.