Search ResiDesk, work history, writing, talks, and tools.
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. The real job is turning that into work people will actually use.
At ResiDesk, that means turning resident texts, reviews, calls, and support threads into answers, the right history, and follow-up a property team can use.
Talking to customers is still business 101. The hard part is hearing the same thing from three places and missing that it is the same thing.
ResiDesk is most of my week. Before that, I worked on outcome-based lending at Climb Credit and advisor tools at BlackRock. Different rooms, same lesson: if the product loses the thread, the whole thing gets brittle.
I care less about the polished demo than the next day. The queue is messy, the edge cases are real, and the person using the product has the final say.
ResiDesk takes most of my week. Before that: Climb Credit and BlackRock. I like work where the customer is already giving you the answer.
Most teams hear plenty from customers. It gets stuck in inboxes, tickets, calls, and support threads before it changes rent, renewals, maintenance, staffing, or how the product works.
The next morning tells you what was real
I care about what survives after the meeting ends: the queue is full, the team is moving, and someone still needs a clear answer.
Do not make the team guess
A good tool helps the team move faster without becoming reckless. It shows policy, history, tone, uncertainty, and the person who owns the next step.
The test is plain
Useful software helps the person doing the work see the customer, what happened, and the next decision before the current process gets in the way.
Start here
[PATHS 03]
Start with the question you came with
You probably came here with one question. Start there; the rest can wait.
We help property teams see the problem before it turns expensive.
Residents are already telling you what is broken. The job is to answer them, spot what keeps repeating, and route the issue to the right person without rereading the whole history.
Looking for
Teams tired of one more inbox.
The best operators already care about retention, NOI, workload, maintenance, and resident trust. The hard part is volume, repetition, and handoff.
Writing about
What happens after the reply.
AI can write a decent reply fast. I still care who owns the next step, what they know, and whether the resident has to repeat themselves.
Where I get skeptical
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 just a cleaner inbox.
Work before ResiDesk
[WORK 04]
A few jobs that shaped this
CompanyWhat I worked onWhat happenedMore
Climb CreditI was CTO and CPO. We put student outcomes into product, data, and underwriting.Annual loan volume grew from $1M to $300M while the product moved closer to graduate outcomes.TechCrunch
BlackRockI worked on product and engineering for advisor tools where interface quality mattered because real money sat behind the decision.The advisor analytics product reached $40M ARR in year one.Work history
ResiDeskI co-founded ResiDesk and spend a lot of my energy on data and product, plus the ordinary founder work of making the company work.Law360 covered a reported 7% lift tied to acting on resident feedback earlier.Law360
Working rules
[MODULE 04]
How I work with the product
My default is simple: talk to the customer, make the work visible, and see what still gets used on a busy day.
01 / Customer
Talk to the customer before the model does anything.
If you have customers, understanding them is business 101. In housing, the hard part is hearing enough residents without making the operator read every thread.
02 / Context
Show me the real job.
Abstractions do not move teams. Give me the stakes, the weird cases, and the person who has to live with the answer.
03 / Adoption
Demos are not adoption. Repeat use decides what is real.
I learned this early at BlackRock: a prototype can win the room and still lose to the spreadsheet people already trust. The test is what they reach for when the meeting is over.
04 / AI
Shorten the distance to a useful answer.
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 call.
05 / Candor
Be direct without making it sharp.
The best teams can say what broke without making it personal. Believe the pain first, then separate what happened from what caused it.
06 / Team
Hire people who can carry the room and keep it moving.
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 clearer by keeping the work simple.
AI in use
[MODULE 05]
What still works the next morning?
AI made it easier to produce a good answer. It did not make it easy to change the work. I care about policy, risk, handoff, trust, and what happens after the answer leaves the screen.
Field note
The product starts after the demo.
I am most useful when a team has a real rollout problem: the demo worked, and the actual day is still fighting back.
01Watch the actual work
Sit with the people doing the job. Watch what they do when the tool is slow, weird, or one more thing to manage.
02Say 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 later. First decide what history it gets, what it can do, who checks it, and where the work goes next.
04Test the cases that break it
Run the cases that make it miss. Show the misses without drama. Then 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 objection, the before state, the number that moved, and the sentence the buyer can repeat without translating it.
$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 only the first question. The buyer, user, reviewer, and executive sponsor are usually worried about different things at the same time.
Field note
Find the work. Show the help. Make the owner clear.
Great demos can still lose to a spreadsheet. Information becomes useful only when the person making the decision can act on it without decoding it first.
01
Discovery that watches the work
Find the narrow place where AI changes the day, not just the story. Talk to the buyer, the user, and the person who gets stuck when it breaks in production.
02
Rollouts you can run again
Make rollouts repeatable: know the job, staff the messy parts, test the risk, and put what you learn back into product.
03
Sales from the thing that actually happened
Help GTM say the true thing: what changed, why the buyer cared, what broke, and what lasted after rollout.
04
Keep the simple version honest
Make it simple without making it fake. I care about the number, the risk, the owner, and the next move.
Resident messages
[MODULE 07]
How a resident message becomes work someone owns
[STEP 01 / LISTEN]
Start with the resident's actual words.
A useful product starts with ordinary apartment 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, and somehow still hard to use.
Texts, reviews, tickets, surveys, renewal notes, and maintenance complaints live in different places. By the time the owner sees the number, it is usually late.
After
The right person sees the issue sooner.
The product brings enough history together to answer, route, report, and show what the building should change while there is still time to change it.
01Message
Resident text, review, ticket, call, survey, or renewal note.
02History
Lease, policy, unit, prior messages, 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 while the problem is still fixable.
About
[MODULE 08]
I did not start in housing
I grew up around research, so the route looked academic at first. I studied applied physics at Cornell because it sounded hard, interesting, and close to real experiments.
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, which made software feel useful instead of theoretical.
The industries changed. The habit 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 clicked when it removed work.
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 did not get the job the first time. Six months later I re-interviewed, moved to New York, and learned that interface quality matters when real money sits behind a decision.
Climb Credit
Outcomes changed what we asked.
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 hear residents better than it does now.
Residents tell buildings what is working and what is not every day. The work is making that clear to owners, usable for operators, and less annoying for the person living there.
Work history
[MODULE 09]
How I got here
The settings changed, but the habit stayed similar: understand what the customer is saying inside a messy process, then build the simplest responsible way to act on it.
Resident-feedback result
7%
That number comes from getting resident feedback into decisions earlier. Law360 wrote up the outside version.
Climb Credit
$1M → $300M
Annual loan volume growth while we pushed outcomes into product, data, and underwriting.
Advisor tools
$40M ARR
Advisor-facing analytics product I helped take from zero to $40 million ARR in its first year.
Co-founder, data and product side, plus the rest of startup life
We help rental-property owners and operators understand what residents ask for across renewals, rent, maintenance, and staffing. The product earns its keep when the right person knows what to do 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 us to put outcomes inside the product, data, and underwriting.
The job was turning institutional infrastructure into a product advisors could use in real conversations. Same information underneath, but useful at the moment someone had to explain, compare, and decide.
More detail
[MODULE 10]
More detail, if useful
What changed
I have worked on products 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 outside links.
TechCrunch covered Climb. Law360, HackerNoon, TechTimes, TechBullion, BuiltWorlds, and 20for20 fill in more of the ResiDesk, applied AI, talks, and property-operations story.
I write when I am trying to make a thought less fuzzy. 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 fewer dropped balls, 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 around the work.
The model is one part. The surrounding tools, guardrails, evaluation, and 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 nobody watching.
FAQ
[MODULE 14]
Fast answers
What kind of AI do I build?
I build AI around work people already have to do. 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 do before ResiDesk?
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 talk about?
I usually come back to the same things: agents, evals, product loops, and the gap between a strong demo and something people still reach for on a busy day. Housing makes this concrete because residents are already telling you what broke.
How do I think about 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 sequence right, testing what good looks like, and keeping a person close enough to stop the product 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 not pretending they know everything yet.
Generic advice is everywhere now. The useful version is specific: here is the customer, here is the constraint, here is the ask, and here is the next decision.
You have real customer pain and need it to change product, GTM, or operations soon.
You are putting AI into work where evals, handoff, and trust are not optional.
You are a housing operator trying to spot resident issues before they become churn or owner surprises.
Probably not the fit
You want generic AI inspiration without a real customer or job attached.
You need someone to bless a demo with no owner, metric, or next step.
You want a broad advisory call without a specific problem to sharpen.
Small tools
[MODULE 17]
Small tools for the curious
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.
02 / Ask this site
Checking browser AI
Ask a concrete question.
The answer uses the copy, talks, writing, links, and tools already 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 the question.
04 / Find what matters
Skip to the parts that matter.
05 / Talk lens
Pull the point from one conversation.
06 / Useful AI check
Paste an AI idea. Check the job.
07 / Tuesday test
Put a demo through a normal Tuesday.
Pick a demo promise and where it has to run. The tool shows what has to be true before it works on a normal day.
Run the test to see what the demo is likely to miss on a real day.
08 / Resident messages
Build a small owner readout.
09 / Pattern check
Show repeated words.
Highlights the words I use a lot here: customer, measurement, handoff, follow-through, trust, and demo.
10 / Page check
Check whether the page is clear.
This checks whether the page is clear, useful, and honest about the work.
11 / Next fix
Pick the next rough spot.
Pick the part that feels roughest and get one concrete next fix.
