I built an AI system that detects revenue leaks in local businesses.
Most B2B outreach is basically guessing.
You scrape a list of companies, send emails, and hope someone happens to need what you sell.
So I built something different.
The system analyzes businesses and tries to detect signals that indicate they’re already losing customers or revenue.
Things like:
• slow websites
• weak review presence
• missing conversion infrastructure
• outdated positioning
Then it ranks companies based on where there’s a clear opportunity.
Instead of cold outreach that sounds like:
“Hey, do you need marketing help?”
You can start with:
“We noticed your competitors are capturing more reviews and your site loads slowly — that’s probably costing you leads.”
Right now the system generates:
• ranked prospect reports
• detected weaknesses
• positioning angles for outreach
Curious if agencies or consultants here would actually use something like this.
Still early, but the idea is replacing random prospecting with market intelligence.
— Ralph
Sounds promising. Tools that help businesses recover lost revenue can have a huge impact.
Interesting idea. Local businesses often don’t realise where they’re leaking revenue until it’s pointed out. What kinds of leaks are you seeing most often?
A few patterns show up pretty consistently.
The biggest one is after-hours lead loss. A surprising number of local businesses still just go straight to voicemail after 5–6pm. For things like legal, HVAC, roofing, etc., a lot of searches actually happen in the evening, so those calls often just go to whoever answers.
Another common one is review velocity vs competitors. It's not just rating — it's how frequently new reviews appear. If a competitor is adding reviews every week and you haven't had one in months, the ranking and trust gap slowly widens.
Unfollowed quote requests show up a lot too. You'll see contact forms or estimate requests on the site, but no obvious follow-up system. Those leads often just disappear.
And then there's site friction — slow pages, clunky mobile forms, or multi-step quote flows that people abandon halfway through.
None of these look dramatic individually, but over a year they can easily add up to tens of thousands in missed revenue.
I'm still experimenting with which of these signals can be reliably detected from public data versus the ones that require internal analytics.
for SaaS specifically the signal is internal — it's the invoice.payment_failed webhook in Stripe. every failed charge fires it. the problem isn't detection, it's that most founders never hook into it, so they have no recovery loop.
i built RecoverKit (tryrecoverkit.com/connect) to solve exactly this: it connects via Stripe OAuth, listens for that webhook, and automatically fires a D+1/D+3/D+7 recovery email sequence. no code, takes 3 minutes to connect.
the external signal you're looking for is actually the aftermath: unusually smooth churn curve (no obvious cancelation spikes), MRR growth that's slower than activation would predict. but once you have webhook access, you can quantify it directly — which is a much stronger selling point than a proxy metric.
That’s a really good breakdown.
The webhook point makes sense — inside the system you can detect the exact invoice.payment_failed event and build a recovery loop around it.
What I find interesting is the outside view of the same pattern. From public signals it just looks like “normal churn,” which is why founders often miss it.
It`s basically the pattern I’m trying to explore with the audit idea — identifying situations where the surface metrics look normal but the underlying system is leaking revenue.
With local businesses the equivalent tends to be things like:
• after-hours calls going to voicemail
• quote requests never getting followed up
• weak review velocity vs competitors
• slow sites causing form abandonment
Individually they don’t look catastrophic, but over a year they add up.
Your example with payment failures is basically the SaaS version of the same phenomenon.
'silent leaks that look like normal churn' is exactly the pattern with failed payments in SaaS.
the payment fails, the customer stops using the product because they lose access, and from the outside it looks like 'churn.' but the customer didn't cancel. they just had a card that got declined and nobody told them. the MRR graph looks the same either way.
the SaaS version of your local business patterns: failed payment going to Stripe's automatic retry only (usually too late), card expiry notifications sent to the wrong email, dunning emails that look like spam so customers don't open them. each one individually small. together they're 5-10% of annual revenue quietly missing.
the detection signal: if your churn rate is unusually smooth (no spikes, no obvious product problems) and your MRR growth is slower than you'd expect, it's worth checking how many 'churned' customers in the last 6 months had a payment failure event right before they left.
Yeah that’s a really good example of the pattern I’m talking about.
A lot of the leaks I’m trying to detect are exactly that type — things that look normal in analytics but aren’t actually healthy behavior.
From the outside it just looks like churn or normal lead flow, but the underlying signal is different.
For local businesses I see similar patterns like:
• after-hours calls going straight to voicemail
• quote requests that never get followed up
• review velocity way below competitors
• slow sites causing form abandonment
Each one individually doesn’t look dramatic, but over a year it can easily add up to tens of thousands in missed revenue.
The interesting challenge for me has been figuring out which signals you can detect purely from public data vs which ones require internal analytics.
The SaaS payment failure pattern is a great example of that.
Love the framing around 'signals before the pitch'. One revenue leak that's almost invisible to founders — especially SaaS — is failed subscription payments. A card expires or gets declined, and unless you have an automated recovery sequence, that customer just quietly churns without ever deciding to cancel. Industry data puts it at 5–9% of MRR lost this way every month. The tricky part is it doesn't show up as a churn event in your analytics — it looks like normal attrition. For local businesses the equivalent might be abandoned quotes or unpaid invoices that nobody follows up on. For SaaS it's the payment processor silently failing. In both cases the leak is fixable once you know where to look.
That’s a really good example.
The interesting ones are the silent leaks because they rarely show up as something obviously broken. From the outside it just looks like “normal” churn or “normal” lead flow.
When you start digging though, you realize a lot of businesses are quietly losing revenue in places they never look.
With local businesses I keep seeing patterns like:
• after-hours inquiries going straight to voicemail
• quote requests that never get followed up
• weak review velocity compared to nearby competitors
• slow sites that cause form abandonment
None of those look dramatic individually, but stacked over a year they can easily turn into tens of thousands in missed revenue.
What I find interesting is figuring out which signals you can detect from public data vs which require internal analytics.
That’s something I’ve been experimenting with lately — building tools that scan public signals and try to estimate where those leaks might exist before you ever talk to the company.
For SaaS it feels trickier though.
Curious — have you seen any good ways to detect failed payment recovery problems without direct access to the billing system? That feels like one of those huge silent leaks but I’m not sure how much of it is observable externally.