The most embarrassing realization I had this week: Our startup was completely invisible to Google.
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?
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