fixRAgent
AI Home Repair Diagnostics
My husband and I spent months bootstrapping fixRAgent. We built a highly secure backend on a private VPS, launched on Product Hunt and hit #44, and started running Meta ads that immediately pulled in enterprise leads. We were feeling incredible about the momentum.
Then I went to organically search for our own site. We weren't even a blip.
No one tells you that hitting publish doesn't actually put you on the internet's radar. I spent hours this weekend falling down a frustrating rabbit hole trying to figure out why Google was ignoring us. I didn't know what a Google Business Profile was. I had no idea Google Search Console existed, or that you literally have to submit a verified sitemap just to tell the algorithm that your website is alive.
Coming from the physical real estate world where a storefront is just visible by default, the lack of a basic checklist for how to make Google actually see your code is wild.
If you are a non-technical founder launching your first SaaS, do not assume the crawlers will just find you. Go set up Search Console, verify your domain, and submit your sitemap on Day 1. Don't wait until you are already hunting for leads to realize your front door is hidden from the street.
What is the most obvious tech-world standard practice that completely blindsided you when you first started?
Hey everyone, we recently launched fixRAgent—an AI-powered home repair assistant—and managed to crack the top 50 globally on PH!
I built this to solve a massive headache in real estate: blind maintenance runs. I recorded a quick demo of our "Home OS" feature in action. It uses AI vision to instantly identify paint codes and exact appliance parts, then catalogs them by property address.
Check out how fast the diagnosis engine works in the video below. Would love to hear what this community thinks of the UI and the flow!
We aren't just building another AI app; we’re solving a cash-flow problem we live every day as property managers.
A $150 service call for a jammed garbage disposal or a tripped GFI is a failure of triage. We built fixRAgent to stop those unnecessary dispatches. By forcing a diagnostic scan before a technician is sent, we ensure the fix is actually necessary—or at the very least, that the tech shows up with the right parts the first time.
This month, we are focusing on onboarding our first 10 landlords to prove the ROI. If we can deflect just one service call per property, we’ve already saved them $1,000+.
If you’re a landlord tired of seeing your margins eaten by simple repairs, we’d love for you to try the tool and give us your 'brutal' feedback.
-
1
Moving from a basic chatbot to a real execution layer that actually stops expensive service calls is exactly why agentic workflows are the future! Forcing a diagnostic scan before dispatching a tech is such a smart, practical way to solve a massive headache. Getting those first 10 landlords on board to prove the ROI is the perfect way to validate it fast in the real world. How are you planning to find and reach out to those initial early adopters?
-
1
Hey Lily, thanks so much! Honestly, we are starting right in our own backyard. Since Russ and I manage our own rental portfolio, we are our own first test subjects.
For the other nine, we are tapping directly into our local network. Between the title companies we use and the other local investors we bump into when buying properties around town, we already know a solid group of landlords who are dealing with the exact same pain of unnecessary dispatch fees. We are just going to buy them a beer, hand them the app, and tell them to try and break it in the real world. We're from a smaller town in Ohio, once we get the data that it's solid, we plan to hit up the bigger cities and see how our system handles their issues .
-
1
Starting with your own portfolio is the ultimate "eat your own dogfood" strategy you feel the pain and the relief first! Buying a beer for local investors is such a smart, low-pressure way to get real feedback from people who actually understand the cost of a wasted $150 service call. Once you prove those margins are safe in a small town, taking that hard data to the big cities will make your sales pitch incredibly hard to ignore. It’s much easier to sell a "money-saver" when you have local receipts to prove it works!
What’s the most common "simple fix" your local landlord friends are most excited to automate away?
-
1
Oh it is definitely a running toilet where the flapper is just stuck open, or a furnace that barely blows any air because the filter is completely clogged. We lose so much money paying an HVAC tech or a plumber a dispatch fee just to show up and swap a $5 filter or push a piece of rubber back down. If the app can just look at the setup and tell the tenant exactly how to check those things themselves before we roll a truck, it pays for itself on the very first use.
-
1
It’s wild how a simple $5 filter or a stuck flapper can turn into a huge bill just because of a dispatch fee! Hearing that the app can pay for itself on the very first use makes it a total no-brainer for any landlord. Empowering tenants to handle those basics themselves is such a smart way to protect your margins. Good luck with those first 10 trials I'm rooting for you!
-
1
Thank you so much!!
-
-
-
-
-
-
1
Most landlords end up losing a huge chunk of their profit to basic maintenance calls that could have been fixed with a thirty-second reset or a simple flip of a switch.
The real drain isn't just the $150 dispatch fee but the "information gap" where the tenant doesn't know what to check and the landlord doesn't want to risk a bigger issue by ignoring it. Using AI to bridge that gap transforms a service request from an expensive guessing game into a filtered, actionable task before the clock even starts on a technician's hourly rate.
Have you thought about how this diagnostic data could be shared directly with the tech beforehand to cut down on those "I need to go to the hardware store" trips?
-
1
That 'information gap' is exactly what we're targeting. The parts run is a massive drain on everyone's time. Right now, when the AI finishes a scan, it outputs a full diagnostic report that includes the specific replacement part numbers. A landlord can simply text or email that link directly to their maintenance guy before he rolls his truck. Instead of a vague 'the fridge is making a noise' work order, the tech shows up with the exact Whirlpool fan motor already in hand. We want to completely kill the phrase 'I have to go to the hardware store and come back'.
-
1
Killing that "hardware store run" is a massive win because that second trip is usually where the labor costs spin out of control for the landlord.
Providing the specific part numbers directly in the diagnostic report creates a level of accountability for the tech that doesn't exist when they are just working off a vague tenant description.
I focus on this type of operational efficiency in my work with high-tier PR and media placement where we use these clear ROI metrics to build brand authority and get companies featured on major news outlets.
Do you have a plan to integrate with parts suppliers like Home Depot or Lowe's so a landlord could actually order the part for pickup while the tech is still en route?
-
1
Absolutely and that is exactly the workflow we built! We actually already have this live. When the AI outputs the exact part numbers, it automatically generates direct links to Home Depot, Lowe's, and Amazon right inside the diagnostic report. The landlord can buy it for in-store pickup with one click before the tech even leaves for the property. I just made a video on how that exact thing works.
https://youtube.com/shorts/RIy_CZ7V2OY?si=8Dsjtg_lhTc-D2EO
If you are interested, check that out. You seem like you get what we're trying to do. We would appreciate any feedback you have.
-
1
The workflow in your video is brilliant because it solves the 'Trust Gap' for landlords. A landlord’s biggest fear is being overcharged for parts they don’t understand, and you’ve just automated that accountability.
From a growth perspective, this specific 'Physical-to-Digital' bridge is a massive authority story. When you feature this logic on high-tier platforms like MSN or AP News, you aren't just getting 'traffic'—you are getting a permanent badge of credibility. For those first 10 landlords you are targeting, seeing fixRAgent featured in major business news makes the decision to join a 'no-brainer.'
More importantly, it builds the brand DNA that AI search engines (LLMs) look for. When an owner asks an AI for 'the most efficient way to handle property maintenance,' these high-authority mentions ensure fixRAgent is recommended as the industry standard.
I’d love to stay in the loop as you onboard those first properties. If you ever want to brainstorm how to turn this ROI data into a full-scale media presence that feeds into AI visibility, let me know!
-
1
Your right! The Trust Gap' is the exact phrasing I've been looking for to describe the core problem we solve!
To be completely transparent, Russ and I definitely want to get this out there in a big way, but we are so new to the distribution side of things. We've spent all our time building the tech, and now we are basically starting from scratch trying to learn exactly how to market it and get it into the hands of property managers.
Since you clearly know this space and have the media connections, if you have any advice on the best ways for a new founder to start bridging that gap—or if you know anyone who loves beta-testing prop-tech—I am absolutely all ears!
I haven't even set up an X or LinkedIn account yet, but I am highly active right here on IH. Would love to hear your thoughts!
-
1
Happy to explain why I suggested LinkedIn first! Most founders think they need to be everywhere but for property tech LinkedIn is where the decision makers actually hang out. Landlords and property managers are there in a business mindset looking for ways to save money which makes them more likely to try your tool.
Facebook and Twitter are great for general buzz but LinkedIn lets you reach property agency owners directly. Since you are starting from scratch focusing on one professional platform will give you better quality feedback from people who actually manage hundreds of doors.
Regarding the media side the best way to bridge the gap is to use your first 10 landlords as a Case Study. Once we have data showing exactly how many hardware store runs you killed and the total dollars saved that becomes the perfect story for a high-tier PR push. That is the kind of authority that makes beta-testers come to you instead of you chasing them.
I would be happy to share a few more specific ideas on how to frame your launch for AI visibility and LLMs once you have those first few properties onboarded. Stay focused on the ROI for now and the distribution will follow!
-
1
Muhammad, I took your advice to heart and just set up my LinkedIn account!
I see exactly what you mean about the focus on ROI. I’m actually working on a short demo video right now to show landlords exactly how this is a game changer for their bottom line—specifically focusing on how we cut out those wasted hardware store runs and misdiagnoses.
I'm going to offer a free month to the first 10 'Founding Landlords' who sign up so we can get that initial data and build those case studies you mentioned. I'm excited and nervous all at the same time.
-
1
The 'Founding Landlord' framing is perfect because it makes early adopters feel like partners in the story, not just beta testers — that psychological shift alone will improve your retention and word-of-mouth.
For the demo video, one specific tip: lead with the dollar amount saved in the first 10 seconds. Not the features, not how it works — just '$150 service call avoided in 3 minutes.' That hooks a property manager immediately because it speaks their language.
Once you have even 2-3 of those case studies with real numbers, that becomes the core of a media story that writes itself. A prop-tech tool that saved landlords X dollars in Y days is exactly the kind of ROI narrative that gets picked up by real estate and business outlets — and that coverage then brings the next wave of landlords to you instead of you chasing them.
I've helped founders package exactly this kind of launch story for major placements. When you're ready to turn those first results into a bigger media push, I'd love to help you put that together.
-
-
-
-
-
-
-
-
6 Comments
The Search Console thing is one of maybe 12 of these waiting.
The pattern's pretty universal: ship something that works in 30 days, then discover the "now what" list around week 4.
Production readiness, observability, billing flows, multi- enancy, SEO, deployment automation, email deliverability. None of it gets taught alongside "build the MVP."
Just spent 15 weeks doing this rebuild path with a real estate SaaS founder who created their MVP on Bolt. Same shape of "wait, I need to handle WHAT also?" energy. The good news: once you've hit 3-4 of these, you start anticipating the next one.
That list of 12 is a classic line-up of invisible walls, Hassan. For us, SEO and email deliverability were the two absolute heavyweights that slammed the brakes on our momentum early on. You spend months building a highly secure backend on a private VPS, and then you discover your transactional mail server is landing straight in spam or your sitemap is totally invisible to Google's crawlers because you missed a configuration step. We’re aggressively knocking those out this week so we can finally start playing offense on multi-tenancy and scaling next. Appreciate the reframe—anticipating the next hurdle makes the chaos feel a lot more systematic.
Multi-tenancy is the right next focus, and honestly one of the more underrated pain points to engineer well. Just spent 15 weeks leading the multi-tenant build for a real estate SaaS this year. Three user roles, Stripe billing with seat-based pricing, Supabase RLS on every query. The non-obvious thing is how much architectural cost compounds if you bolt tenancy on later vs baking it in from the schema up. Worth getting right the first time. Good problem to be entering.
You aren't kidding about those architectural cost compounds. We're actually leaning heavily into Supabase RLS on our roadmap precisely because retrofitting it into an existing schema is a nightmare. Doing the un-sexy database plumbing upfront feels slow, but it's the only way to scale multi-tenancy securely without hitting a brick wall later. Really appreciate the validation from someone who just went through the 15-week trenches on it
The performance trap is the next one to watch for. Supabase RLS runs the policy on every query, which is fine until you have nested joins or you're filtering on columns the policy itself needs to evaluate. Few things worth doing upfront that save pain later: make sure tenant_id is a real column on every table that gets queried (don't try to derive it through joins), add composite indexes with tenant_id as the leading column, and benchmark with realistic tenant counts (not just one tenant in dev) before you commit to a policy shape.
The other thing nobody warns you about: testing RLS for tenant isolation is its own small engineering project. Easy to ship a policy that "looks right" but leaks data on a specific query path. We ended up writing isolation tests that simulate one tenant's session and assert they can never see another tenant's rows across every endpoint. Saved us at least one incident.
You're thinking about it in the right order though. Most teams hit this stuff the hard way.
Hardcoding tenant_id directly to avoid that nested join latency trap is an absolute gold nugget of advice. It’s exactly the kind of un-sexy database plumbing that saves your application speed before you hit a brick wall at scale.
Your point about tenant isolation testing being its own standalone engineering project hits incredibly close to home. Shipping a policy that 'looks right' but silently leaks data under a specific nested query path is a major anxiety for us, especially as we map out our partner API layers right now.
Out of curiosity, what did you end up using to build out your isolation testing framework? Did you write custom scripts to simulate those individual tenant sessions, or did you leverage a specific framework to assert those row-level barriers across every endpoint? That 'saved us at least one incident' line is exactly why we want to build this protective fence early