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Founder, builder, customer work
[SURFACE 01]
Texts, tickets, reviews, calls
Arjun Kannan
I build software for teams where customers are already telling us what is broken, and the hard part is getting that into the work fast enough to matter.
At ResiDesk, that means turning resident texts, reviews, calls, and support threads into answers, context, and follow-up a property team can actually use.
Talking to your customer is still business 101. The harder version is when the customer has already talked to you 10,000 times and nobody can see the pattern quickly enough.
Most of my week is ResiDesk. Before that, I worked on outcome-based lending at Climb Credit and advisor tools at BlackRock. Different rooms, same lesson: understand the customer or the system gets brittle.
I care less about a polished demo than the day after. The queue is messy, the edge cases are real, and the person using the system gets the vote.
Most of my week is ResiDesk. Before that: Climb Credit and BlackRock. I like work where the customer is already giving you the answer if you know how to listen.
Most teams are not missing feedback. It is sitting in inboxes, tickets, calls, and support threads before it ever changes rent, renewals, maintenance, staffing, or the product.
The next morning is the test
I care about what survives after the room clears: the queue is full, the team is moving, and a customer is still waiting for a real answer.
Do not leave the team guessing
A good system helps the team move faster without becoming reckless. It shows policy, history, tone, uncertainty, and who owns the next step.
The bar is simple
Useful software helps the person doing the work see the customer, the context, and the next decision faster than the current process.
Start here
[PATHS 03]
Start with the thing you care about
Most people come here with a real question. Pick the path that gets you there fastest.
We help property teams see the problem while there is still time to act.
Residents are already explaining what is broken. The job is to answer them, see the pattern, and get the issue to the right person without rereading the whole history.
Looking for
Teams that do not want another chat surface.
The best operators already care about retention, NOI, workload, maintenance, and resident trust. The problem is not caring. It is volume and repetition.
Writing about
What happens after the reply.
AI can write a plausible reply fast. I still care who owns the next step, what they know, and whether the resident has to explain everything twice.
Not useful
Replies are not the finish line.
If the reply goes out and nothing changes, I do not trust the product yet. That is not a finished job. It is a cleaner inbox.
Work before ResiDesk
[WORK 04]
A few rooms I learned in
CompanyWhat I worked onWhat happenedMore context
Climb CreditI was CTO and CPO. We built student outcomes into product, data, and underwriting.Annual loan volume grew from $1M to $300M while we moved the product toward outcomes after graduation.TechCrunch
BlackRockI worked on product and engineering for advisor tools where interface quality mattered because real money was behind the decision.The advisor analytics product reached $40M ARR in its first year.Work history
ResiDeskI co-founded ResiDesk and spend a lot of my energy on data and product, plus the normal founder work of making the company move.Law360 covered a reported 7% lift tied to acting on resident feedback sooner.Law360
How I think
[MODULE 04]
How I work
My default is simple: talk to the customer, make the work visible, and see what still holds when the day gets loud.
01 / Customer
Talk to the customer before the model.
If you have customers, understanding them is business 101. In housing, the hard part is hearing enough residents without dropping every thread on an operator.
02 / Context
Show me the actual work.
Abstractions do not move teams. Give me the stakes, the edge cases, and the person who has to live with the result.
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. The test is what people reach for after the meeting.
04 / AI
Shorten the distance.
I do not need AI to do everything. I need it to move something from stuck to almost done while a person still owns the judgment.
05 / Candor
Be direct about the system.
The best teams can say what is broken without making it personal. Believe the pain, then find what actually caused it and who can change it.
06 / Team
Hire people who can carry context.
The best people I have worked with can walk into a messy situation, find the few facts that matter, and move without waiting for a perfect script. They make the team calmer by making the work clearer.
AI deployment
[MODULE 05]
What still works the next day?
AI made it easier to sound useful. It did not make it easy to change work. I care about policy, risk, handoff, trust, and what happens in the queue after launch.
Deployment note
The next day is when the product starts.
I am most useful when a team has a real deployment problem: the demo worked, and the day still pushes back.
01Find the real job
Sit with the people doing the job. Watch what they do when the system is slow, weird, or awkward to use.
02Name what changes
Check whether it saves time, reduces risk, protects retention, frees capacity, or earns trust. If nothing changes, call it a demo.
03Design around the job
Pick the model late. First decide context, tools, permissions, evals, review points, and the handoff into the work people already run.
04Test the ugly cases
Run the ugly cases. Show the misses without drama. Measure whether the work moved faster, got safer, or reached the right person sooner.
05Bring the rollout back to product
Sales credibility comes from what actually happened: the real objection, the before state, the adoption metric, and the sentence the buyer can repeat without squinting.
$40M ARRBlackRock advisor analytics, first year
$300MAnnual loan volume at Climb Credit
100+Startup investments and founder calls
AI product work
[MODULE 06]
The demo is the first fifteen minutes
Capability is not the finish line. The buyer, user, reviewer, and executive sponsor are usually worried about different things.
Field note
Find the work. Show the help. Make ownership clear.
Great demos can still lose to a spreadsheet. Information becomes useful only when the person making the decision can act on it.
01
Discovery that sees the work
Find the narrow place where AI changes the day, not the slide. Talk to the buyer, the user, and the person who gets stuck when it breaks.
02
Deployment that repeats
Make deployments repeatable: scope the job, staff it, test it, review risk, and put what you learn back into product.
03
Sales from what actually happened
Help GTM say the true thing: what changed, why the buyer cared, what broke, and what lasted after rollout.
04
Executive translation without fog
Make the executive version clear without flattening the technical truth. I care about the number, the risk, the owner, and the next move.
Resident messages
[MODULE 07]
How one resident message becomes work someone owns
[STEP 01 / LISTEN]
Start with the resident's actual words.
A useful system starts with ordinary pain: a broken washer, a pet-policy question, Wi-Fi complaints, package-room messes, and early signs someone may not renew.
Daily textsReal complaints
ResiDesk
[LOOP 08]
Why housing is worth the work
Before
Resident feedback is everywhere.
Texts, reviews, tickets, surveys, renewal notes, and maintenance complaints live in different places. The owner usually sees the financial result too late.
After
The right person sees the issue sooner.
The system holds enough context to answer, route, report, and show what the building should change before the issue gets expensive.
01Message
Resident text, review, ticket, call, survey, or renewal note.
02Context
Lease, policy, unit, history, tone, and what happened before.
03Owner
The person or team that can actually change what happens.
04Action
Answer, escalate, repair, explain, or change the policy.
05Report
What owners need to see about retention, NOI, workload, and risk before it is too late.
About
[MODULE 08]
I did not start with housing
I grew up around research, so the path looked academic at first. I studied applied physics at Cornell because I liked real systems, messy measurement, and small details that changed the answer.
Software came in sideways. I was in an electron microscopy lab and wrote code to speed up a magnetic-noise setup. It saved hours quickly. That changed software from coursework into leverage.
The industries changed. The question 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 teams hear residents clearly enough to act.
Physics
Software became the way in.
I wrote a tool in an electron microscopy lab to speed up a magnetic-noise setup. It saved enough time that software stopped feeling like coursework and started feeling like leverage.
BlackRock
Real stakes make the interface matter.
I came back six months later, re-interviewed, and moved to New York. It taught me that interface quality matters when real money sits behind a decision.
Climb Credit
Outcomes changed the product conversation.
Instead of asking who looked safest on paper, we asked what happened to earnings after the program. That pushed outcomes into underwriting, product, and data, and annual loan volume moved 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 clear to owners, useful to operators, and less annoying for the person living there.
Work history
[MODULE 09]
How I got here
The settings changed, but the job stayed similar: understand what the customer is saying inside a messy process, then build the simplest responsible way to act on it.
ResiDesk customer result
7%
That number depends on getting resident feedback into decisions earlier. Law360 has the outside writeup.
Climb Credit
$1M → $300M
Annual loan volume growth while outcomes became part of product, data, and underwriting.
Advisor tools
$40M ARR
Advisor-facing analytics product I worked on from zero to $40 million ARR in its first year.
We help rental-property owners and operators understand what residents ask for across renewals, rent, maintenance, and staffing. The product earns its keep when ownership is clear and the work gets done before the next messy thread appears.
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, data, and underwriting.
The job was turning institutional infrastructure into a product advisors could use in real conversations. Same information underneath, but usable at the moment someone had to explain, compare, and decide.
More background
[MODULE 10]
Work, writing, and the longer version
Company building
I have worked where the number had to move.
BlackRock advisor analytics reached $40 million ARR in the first year. At Climb, annual loan volume grew from $1 million to $300 million as outcomes moved into product and data.
If you want the longer version, start with the links.
TechCrunch covered Climb. Law360, HackerNoon, TechTimes, TechBullion, BuiltWorlds, and 20for20 have more on ResiDesk, applied AI, talks, and property operations.
I write when I am trying to think something through. Most pieces come back to the same test: does this help someone finish the work, or did we just make the demo easier to sell?
If a tool does not help someone finish a real task sooner, with less context loss, it is hard for me to care about it.
Understand the job first.
If you do not know what someone is actually trying to do, you are probably just rearranging the screen.
Build the system around the model.
The model is one part. Context, tools, guardrails, evaluation, and the handoff into someone's day decide whether it changes anything.
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 14]
A few fast answers
What AI systems do I actually build?
I build AI around work that already exists. At ResiDesk, that means helping property teams answer residents, understand what is happening in the building, and get the right issue to someone who can fix it.
What did I work on before ResiDesk?
I worked at Climb Credit and BlackRock. At Climb, I helped annual loan volume grow from $1 million to $300 million. At BlackRock, I worked on a retail analytics product that reached $40 million ARR in its first year.
What do I usually write and speak about?
I usually come back to the same things: agents, evals, product loops, and the gap between a strong demo and something people still use when the day gets messy. Housing makes this concrete because the customer is already talking.
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 the context right, testing what good looks like, and keeping a human close enough to stop the system from automating the wrong thing.
Investing
[MODULE 15]
Investing when I can be useful
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.
Generic advice is free now. The useful version is specific: here is the customer, here is the constraint, here is the ask, here is the next decision.
You have real customer pain and need it to change product, GTM, or operations.
You are deploying AI where context, evals, handoff, and trust actually matter.
You are a housing operator trying to spot resident issues before they become churn.
Probably not a fit
You want generic AI inspiration without a real customer or job attached.
You need someone to bless a demo that has no owner, metric, or next step.
You want a broad advisory call instead of a specific problem I can help sharpen.
Optional tools
[MODULE 17]
Optional tools, after the story
01 / Page map
Local visuals ready
Map the work.
Pick a view. The graphic runs anywhere. If the browser has a local model, it can add a sharper read on fit or adoption.
02 / Ask this site
Checking browser AI
Ask the site a question.
The answer uses the copy, talks, writing, links, and tools on this page. Try: "why ResiDesk?", "what still works the next day?", "where should I start?"
Try asking about ResiDesk, founder advice, BlackRock, Climb, writing, or the Tuesday test.
03 / Conversation map
Start with a real question.
04 / Find the relevant bits
Find the parts worth reading.
05 / Transcript lens
Pull the useful parts from a conversation.
06 / Useful AI test
Paste an AI idea. Check whether it has a job.
07 / Tuesday test
Stress-test a demo against a normal Tuesday.
Pick a demo promise and an environment. The simulator shows what has to be true before it works on a normal day.
Run the test to see demo risks, workday pressure, and the adoption check.
08 / Resident messages
Make a small building readout.
09 / Pattern highlighter
Show the pattern inside the page.
Highlights the words I keep coming back to: customer, context, measurement, follow-through, trust, and demo.
10 / Internal check
Audit the page against its job.
This checks whether the page is clear, useful, and honest about what it is trying to do.
11 / Next pass
Pick the next weak spot.
Pick the part that feels weakest and get a concrete next pass.
