EliseAI
EliseAI Innovation & Technology Culture
Frequently Asked Questions
EliseAI ships constantly — engineering deploys 50–100 times a day, and the company runs over 7 billion tokens daily across voice, email, and text while maintaining margins. The technology goes well beyond chatbots that just acknowledge a request: EliseAI's agents complete entire workflows end-to-end, from scheduling a tour to resolving a maintenance ticket to adjusting a resident's ledger, without a human in the loop for most of it. The research side runs real experiments, not just feature releases — testing things like regional voice personalization to improve conversion, training proprietary models to cut latency and own more of the stack, and partnering with frontier labs like OpenAI, who's recognized EliseAI as a trusted partner in healthcare AI. Innovation here shows up as continuous testing and iteration, not a single headline launch.
EliseAI is setting the pace competitors are chasing. The company processes millions of conversations a month across voice, text, email, and chat, and has increasingly moved toward developing its own models rather than relying entirely on third-party APIs, specifically to control latency and cost at scale most competitors haven't reached yet. That matters because the products sit on the critical path of live customer interactions. The infrastructure also has to keep pace with how fast the company ships — deploying 50–100 times a day means the underlying systems are built for continuous iteration, not quarterly release cycles. While most players in housing and healthcare AI are still figuring out their first deployment, EliseAI is already iterating on its third or fourth generation of the same workflow.
EliseAI works directly with the labs building frontier AI — Baseten, Anthropic, OpenAI, ElevenLabs, and Cartesia — which means access to new tech and models ahead of public release, and the ability to get custom builds when off-the-shelf isn't fast or precise enough. That's paired with deep integration partnerships like Zillow, putting EliseAI's technology directly into the housing ecosystem at scale.
Engineers here describe a culture built on trust and speed rather than process. Most new hires are surprised to find themselves pushing code on day one — the bar is high, but so is the trust extended from the start. Engineering leadership has been direct that engineers aren't treated as implementers waiting for spec; they're expected to be in the driver's seat on product direction, not just execution. There's no fixed roadmap in the traditional sense, and the team sees that flexibility as a real advantage — small, autonomous teams shipping 50 to 100 times a day, compounding fast because nobody's waiting on a quarterly plan to move. Failure isn't something to avoid here — if nothing's breaking, the team isn't moving fast enough. Calculated risk-taking on things nobody's tried before is treated as the job, not an exception to it. And because EliseAI operates as a vertical AI company across two enormous, complicated industries, the sheer scope of what gets built is hard to overstate — a single weekend project here could be its own startup elsewhere.
The short version: EliseAI doesn't treat innovation as a department or an initiative — it's the operating model, and that traces directly back to the founders. Minna and Tony are both software engineers, which is why building, shipping, and testing constantly isn't a value statement on a wall. Real experiments, fast shipping, direct access to frontier labs, and engineers trusted to own product direction all point the same way: this is a company defining where the industry goes next.
EliseAI Employee Perspectives
What project are you most excited to work on in 2025?
I’m most excited to continue building out our centralized CRM platform with AI integrations that genuinely help agents save time on repetitive, manual tasks, allowing them to focus more on operations that drive community success. EliseAI is in an exceptionally rare position where we’ve spent years developing effective and practical generative AI, and now we face the much easier task of building a CRM on top of those tools, rather than the other way around.
Many of our customers have shared how the recent AI hype has resulted in a flood of so-called “time-saving tools” that, in reality, are nothing more than basic email suggestion generators. Understandably, this has led to AI fatigue amongst our customers. That’s why it’s incredibly rewarding to demonstrate first-hand how our CRM truly saves time — by surfacing only the tasks that need attention, optimizing tour schedules and enabling bulk actions for common, repetitive workflows.
What does the roadmap for this project look like?
Currently, I see this project being a major part of my day-to-day over the next three months, given our current goals and commitments to our partners. One of the most enjoyable aspects of this project is the high level of collaboration as it involves working with nearly every department at EliseAI, including design, operations, strategy and product while also iterating with external developer teams for integrations and gathering customer feedback on a weekly basis.
At this point, we’ve addressed every foreseeable roadblock. However, with any project of this scale, there will be surprises along the way. More than anything, I’m excited to tackle those challenges because so many people are invested in this project. I have full confidence that our team will collaborate to develop innovative solutions that continue advancing technology in the housing industry.
What in your past projects, education or work history best prepares you to tackle this project? What do you hope to learn from this work to apply in the future?
Over the past year at EliseAI, the most valuable preparation I’ve had has come from two key factors: the ability to easily engage with our customers and a strong focus on developer velocity. From my very first month on the team, I was on calls with customers at every level of the industry and meeting with clients in person across the country. This experience has given me a much deeper understanding of which problems need to be solved and why they are important.
In my experience, there’s only so much context that can be gained from reading a lengthy list of product requirements. However, being able to ask real-time questions to the people who use the platform daily and understanding what is and isn’t important to them significantly improves our ability to build the best possible product. It also greatly reduces the number of times we have to rework a finished feature, a frustration every engineer knows all too well.
Responses have been edited for length and clarity. Images provided by Shutterstock and listed companies.
EliseAI's Tech Stack
