How I Built a Tool That Warns Me 60 Seconds Before My Site Goes Down
We launched today on Product Hunt:
https://www.producthunt.com/products/orvo-ai?utm_source=other&utm_medium=social
Would love honest feedback from fellow founders.
Appreciate any thoughts — especially critical ones.
#SaaS #Monitoring #WebDev #Programming #DevOps
Most monitoring tools ask one question: “Is it up?”
I wanted to answer a different one: “Is it about to go down?”
I kept getting “your site is down” emails — from users, not my monitoring tools. By the time I got the alert, users were already frustrated, support tickets piled up, and revenue was leaking.
That’s when I built ORVO AI, a tiny predictive engine that flags instability 60–90 seconds before downtime.
Why This Matters
A small early-warning window makes a huge difference:
Scale servers before a crash
Trigger failover
Alert your team before users even notice
Traditional uptime monitoring only reacts after the fire starts. ORVO AI gives you time to prevent it.
How It Works (Simplified)
Tracks trends + volatility in response times
Generates a risk score (0–100)
No heavy ML, no GPU, no Kubernetes — just fast math
In stress tests, it reliably warns 60–90 seconds before failure while the site is still technically “up.”
Built With
Node.js + TypeScript, SQLite, single VPS (~$6/month)
Sometimes simplicity wins.
I’m sharing this on IndieHackers because feedback from fellow founders and makers is invaluable.
Question: How do you get early warnings for your production systems?
Pricing:
• Free 14‑day trial — $0 (no cc)
• Pro — $19/month
• Business — $49/month
• Enterprise — custom
This is smart.
I learned the hard way that “site is down” alerts are already too late. By the time you get them, users are angry and damage is done. Even 60 seconds early can save a lot of stress.
I like that you kept it simple instead of overbuilding with heavy ML. One suggestion: show a few real examples or case studies where it actually prevented downtime — that proof will help people trust it faster.
Early warning > post-mortem every time.
Really appreciate that.
You’re exactly right once the “site is down” alert fires, you’re already in damage control mode. The goal is to catch the instability phase before it tips over.
And good point on proof. I’m working on documenting a few real scenarios where early deviation patterns showed up before failure sharing those will definitely make it clearer how it behaves in practice.
Early warning > post-mortem, 100%.
"The reactive vs predictive framing is what got me here."
Most devs (including me) set up uptime monitoring and forget about it — until users start complaining. The 60-second early warning window is a genuinely different approach.
Curious — how does the risk score behave on sites with naturally spiky traffic? Like if a blog post goes viral and response times jump suddenly, does it flag a false positive?
I'm building a content automation site myself and downtime during a Vercel deploy + Supabase query spike is something I haven't solved cleanly yet. This might be exactly what I need.
Great question! The 88 Engine uses EMA smoothing specifically to handle spiky traffic. A single sudden spike won't trigger an alert it looks for sustained trend changes over multiple consecutive checks. So a viral blog post causing a temporary jump gets filtered out as noise, but a gradual climb from 200ms → 400ms → 800ms over several minutes gets flagged as real degradation.
For your Vercel + Supabase setup, ORVO AI could help catch those deploy-related slowdowns early. Would love to have you try it and see how the risk score handles your traffic patterns that kind of real-world feedback is exactly what makes the engine smarter.
What's your biggest challenge to achieve success at the moment?
Honestly? Time. 😅
Balancing a full-time job, family, and then carving out time to build something on the side is the real challenge. By the time evening comes, energy is already half gone.
But I think that's also what makes it interesting — building under constraints forces you to be lean and focused. No time to overthink, just ship.
Currently building PilotStack on weekends and late nights — slow but moving! 🚀
Good question and sorry, I realized I didn’t actually address what you asked earlier.
The risk score isn’t triggered by a single spike. It looks at short rolling patterns (latency + error rate together), so if traffic jumps but stabilizes quickly, it shouldn’t flag as critical.
Where it does trigger is when response time increases in a way that historically precedes failures especially when paired with error drift.
Your Vercel deploy and supabase spike case is interesting. Are you seeing gradual latency creep before downtime, or does it fail almost instantly? That distinction matters a lot.
Honestly can't give you a definitive answer on that yet — we're still in the early stages so the traffic load hasn't been significant enough to trigger a clear pattern. The errors we've seen have been mixed, nothing consistent enough to confidently say it's gradual creep vs instant failure. That said, neither Vercel nor Supabase has actually let us down so far — both have been solid.
Will definitely keep an eye on that distinction as we scale up though, good thing to watch for.
That makes sense, early stage is actually the best time to observe patterns before things get noisy.
One thing that might be worth checking as you scale is whether latency starts drifting before error rates move. In a lot of systems, that’s the early signal that something’s about to tip.
If you ever start seeing weird behavior during deploy spikes, I’d be curious to compare notes. Those edge cases are usually where predictive monitoring proves itself.