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• outdated positioning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
it can be best move for local businesses .
Small build update since the last post.
The project is starting to shift from a collection of scripts into something that actually feels like a system instead of a bunch of tools duct-taped together.
Originally the pipeline was very linear and CSV-driven:
hunter → scout → signals → audit → pitch
That worked fine for early prototyping, but once I started running the pipeline across larger sets of companies it became obvious that passing artifacts around as CSVs wasn’t going to scale very well.
So the current focus is moving the stack toward a more structured architecture.
Right now I’m working on:
• migrating the pipeline state off CSVs and into Postgres
• tightening the signal definitions so the system surfaces consistent patterns instead of just descriptive observations
• separating signal detection → scoring → narrative/report generation into distinct stages
• building a simple operator dashboard to inspect runs and audit outputs
• turning the audit output into a structured report generator
One thing that’s becoming clearer as the system evolves is that the interesting part isn’t really “lead generation”.
The real value is the ability to detect measurable opportunity signals across businesses and surface them in a way that’s actually actionable.
Instead of a list of contacts you end up with something closer to:
“These companies are very likely losing customers online, and here are the specific signals indicating why.”
That shift is making the project feel less like a prospecting tool and more like an opportunity intelligence engine.
Also a quick transparency note from earlier in the thread:
Part of the codebase currently references a Stripe integration example that another builder shared. I made sure it is not using anyone else’s Stripe keys or account, it was only used as a reference for implementation during testing.
I’m autistic and sometimes take things very literally, so I interpreted the earlier message as permission to experiment with the pattern for testing. If that ever becomes an issue I’ll remove it immediately — I want to make sure I’m respecting the intent and crediting people properly. @heze
Still early, but the pipeline is starting to stabilize and the architecture is slowly turning into something that could run like a platform instead of a pile of scripts.
Appreciate all the feedback from everyone here — it’s genuinely helped shape where this project is going.
The "signal-based outreach" angle is genuinely differentiated — starting a conversation from a detected weakness is a much stronger opener than generic cold email.
One thing that could sharpen the AI analysis: the prompts driving the weakness detection matter a lot. If you're using a general-purpose prompt to identify "slow website + weak reviews", you'll get generic outputs. Structuring it with explicit constraint blocks (what counts as weak in which context, how to rank severity, what's actionable vs. not) tends to produce much more precise and consistent signals.
Building in a similar direction with flompt — a visual prompt builder for structuring exactly that kind of complex analysis instruction. Happy to compare notes. And if you try it, a ⭐ on github.com/Nyrok/flompt would mean a lot — solo founder here too 🙏
That’s a really good point about constraint blocks. The difference between a generic “find weaknesses” prompt and structured detection rules is something I’m starting to see more clearly as I experiment with this.
Right now I’m trying to tighten the signal definitions so the analysis isn’t just descriptive but actually surfaces consistent, actionable patterns.
I’ll check out flompt as well — structured prompt tooling seems like it could be useful for testing different detection frameworks.
Curious: when you structure prompts this way, do you usually separate the signal detection from the explanation/report generation, or keep it in a single pass?
Great idea! What's your main acquisition channel?
Great question. Right now I’m testing two paths.
The first is very targeted outreach to businesses where the signals are obvious (traffic decline, poor site performance, missing SEO fundamentals). Instead of selling a service, I’m sending them a short intelligence report showing what’s likely costing them customers.
The second is communities like Indie Hackers where people understand the problem space and can give honest feedback early.
The long-term goal is to automate the discovery side so the system itself identifies businesses with measurable opportunity signals and generates a report explaining the issue.
Curious — have you seen similar “opportunity signals” in businesses you’ve worked with?
I see we think alike.... i built mines and ran it already to do this and more. good luck.
That’s awesome — I’d actually love to hear how it went.
What signals ended up being the most useful in practice?
The interesting thing I’m seeing so far is that a lot of businesses have obvious opportunity signals (traffic decline, performance issues, missing SEO basics), but nobody is actually surfacing them in a way that’s actionable for the owner.
Great idea, and putting it into action with local businesses one by one will reveal the true path long term. As you agentify each workflow, you will be able to have high throughput and increase your revenue.
Great point. That’s actually the path I’m trying to follow.
Right now I’m intentionally keeping it very manual so I can understand which signals actually matter in the real world versus what just looks good in theory.
If I can reliably identify a handful of meaningful signals (traffic drops, performance issues, missing SEO fundamentals, etc.), then those workflows can eventually be automated and chained together.
The interesting part is figuring out which signals actually correlate with real lost revenue for businesses.
Have you seen any signals like that show up consistently in companies you’ve worked with?
Your detection signal is exactly right, and it's underused.
Most founders look at churn charts and see a smooth curve. That smoothness is actually suspicious — real voluntary churn tends to spike after onboarding failures, pricing changes, or competitor launches. Consistently smooth churn with MRR growth slower than acquisition would predict often has involuntary churn as the invisible floor.
The cleanest way to verify: pull your Stripe customers who 'churned' in the last 6 months and check if they have a invoice.payment_failed event within 7 days before their subscription ended. If 20%+ of them do, you have a recoverable problem — not a product or retention problem.
RecoverKit does exactly the automation around this (tryrecoverkit.com/connect). Still in beta, free right now. Given your focus on revenue leaks this seems directly in your territory — would be curious to hear what you find if you run that check on your own data or clients'.
Thanks for sharing that — the “invisible floor” concept in churn is a really interesting way to look at it.
Most of what I’m looking at right now is external signals (traffic patterns, site performance, search visibility, etc.), so I haven’t run that Stripe-style check yet, but the idea of structural signals revealing hidden problems resonates a lot.
It seems like a lot of revenue leaks show up as subtle patterns long before they become obvious failures.
I’m curious — have you found that those payment failure signals show up consistently across different types of businesses, or mostly SaaS?
Sounds promising. Tools that help businesses recover lost revenue can have a huge impact.
The tricky part is actually detecting the leaks from the outside.
Some signals are public (review velocity, page speed, directory presence), but others you can only infer indirectly — like weak after-hours capture or slow lead response.
That’s where most of the experimentation has been.
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.
That’s really interesting. The after-hours lead loss point makes a lot of sense ....a lot of local businesses probably don’t realise how many potential customers they miss that way. Curious whether most owners are surprised when they see the data?
Yeah, most of the time they are. The interesting part is that the leaks usually don’t look like problems from the owner’s perspective.
A business might still be getting calls during the day, so it feels like things are working. But when you step back and look at the signals — things like evening searches or unanswered after-hours calls — you start to see how many potential customers probably never make it through.
What surprised me is how often the issues are small operational gaps rather than something obviously broken. Individually they seem minor, but over a year they can quietly add up to a lot of missed revenue.
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.