Someone just commented on an article about your industry. They told you exactly what they need.

We scan articles and comment sections for people discussing topics your buyers care about. Then we extract their name, company, and verified email — scored by confidence so you know which ones are real.

Start Finding Active Buyers See How It Works

No credit card required. Intent data from real conversations.

See why job title targeting misses the people actually ready to buy

Your best prospects are telling the internet what they need. You're emailing strangers instead.

The old way

Buy a lead list based on job title — no idea if they're actually in-market
Mass email thousands of people who never asked for what you sell
Reply rates under 1% because there's no intent signal
Manually browse forums trying to find people asking questions — takes hours
No way to get contact info from a commenter's profile
Even if you find an interested person, you're guessing their email

With comment scanning

Target people who just demonstrated interest by engaging with relevant content
Every contact commented on a topic your buyers care about — they opted in with their behavior
5 email sources checked in sequence — if the first doesn't find it, the next one does
Each email gets a confidence score from 0-100 so you know how much to trust it
Company, job title, and LinkedIn profile extracted automatically
Export scored contact lists ready for outreach — no manual enrichment needed

Here's exactly how a topic becomes a scored contact list

Three steps. From topic to verified contact list.

You pick the topics. We find the people engaging with them and deliver their contact info — scored.

Pick Your Topics

Tell us what your buyers talk about. "Cold email strategy." "How to buy a business." "CRM migration." The system generates search variations and finds articles with active comment sections across platforms.

We Extract Every Commenter

Articles get scraped for every comment. Each commenter's profile is parsed for their name, headline, and company. Then the multi-source email pipeline runs — checking their profile, company website, Google, email format databases, and contact APIs in sequence.

Get Scored Contacts

Every email gets a confidence score from 0-100 based on name match, domain match, and source reliability. You decide your threshold — export only 90+ scores for high confidence, or cast wider at 70+. Each contact includes the article they engaged with.

What the pipeline delivers

Real contacts who commented on articles about "how to buy a business." Each email is scored by confidence.

Name	Company	Title	Email	Score	Source	Article Topic
Jay F.	Meridian Capital	Managing Director	`[email protected]`	100	Profile	5 Steps to Buying Your First Business
Rachel M.	Greenleaf Advisors	Principal	`[email protected]`	95	Format	Due Diligence Checklist for SMB Acquisitions
Carlos D.	BrightPath Holdings	VP Operations	`[email protected]`	100	Company	Why Now Is the Time to Acquire a Local Business
Amir K.	Nexus Ventures	Analyst	`[email protected]`	80	Profile	Financing Options for First-Time Buyers
Lisa T.	Apex Growth Partners	Director of Strategy	`[email protected]`	70	Search	The Hidden Costs of Buying a Business

This is 5 of 83 scored contacts from a single topic. Scores 90+ mean name and domain both matched.

Email sources checked per person

0–100

Confidence score per email

200+

Commenters per topic avg.

50–100

High-confidence contacts per run

Start Finding Active Buyers

Free to try. No credit card required.

Here's what makes multi-source email discovery different from single-lookup tools

Not a lead list. Intent-verified contacts with scored emails.

The difference between emailing someone with the right job title and emailing someone who just told you what they need.

Smart Query Generation

Enter a topic like "cold email" and the system generates dozens of search variations — combining your keywords with date modifiers, name variations, and platform operators. It's not one search. It's a systematic sweep that surfaces articles from every angle, finding comment sections that a manual search would miss entirely.

5-Source Email Waterfall

Most tools check one database. We check five, in sequence. First the commenter's own profile. Then their company website. Then Google search. Then email format databases with pattern matching. Then contact APIs. Each source adds candidates. The algorithm scores every email found by checking if the person's name and company domain both match — and picks the highest-scoring one.

Confidence Scoring (0–100)

Every email gets a confidence score. A score of 100 means the person's name and company domain both matched from a reliable source — that's a verified contact. A 70 means the email format matches known patterns at their company. You choose your threshold. Export only 90+ for laser-targeted outreach, or go wider at 70+ when you want volume.

Full Profile Enrichment

A comment gives you a name and a headline. The pipeline turns that into first name, last name, job title, company name, company website, and company domain. If the company has a website, we scrape it for additional team member emails. If they're on a contact API, we pull that too. By the time it reaches your contact list, it's a complete lead record.

Start Scanning Comments

Real intent data. Scored emails. No guessing.

How does this compare to buying a lead list?

Honest comparison: intent scanning vs. lead lists.

Lead lists give you job titles. Comment scanning gives you people who just told you what they care about.

	Comment Scanning	Lead list providers	Manual forum research	Social listening tools
Intent signal	Person engaged with relevant content Behavioral intent, not inferred	Job title match only No intent data	Strong, if you find them	Brand mentions Not purchase intent
Email discovery	5 sources, scored 0–100 Waterfall pipeline	Single database Often outdated	None — manual lookup	None
Company enrichment	Profile + website + domain Auto-scraped	Basic company name	Whatever you can find	Company name only
Email confidence	Scored 0–100 per email Name + domain match validation	Binary valid/invalid	You guess	N/A
Export ready	Contact list with all fields Name, email, company, score, source	CSV download	Spreadsheet you built	Dashboard only

Lead lists give you people with the right job title. Comment scanning gives you people who just demonstrated interest in the exact topic your product solves — with verified emails attached.

Still not sure? There's no risk in trying.

No credit card required

Enter a topic, let the pipeline run, and see real scored contacts before you're ever asked to pay. Judge the quality yourself.

Cancel anytime

No contracts, no annual lock-in. Your exported contact lists and enriched leads are yours to keep regardless.

Contacts within hours

Pick a topic, let the pipeline scan articles and extract commenters, and get back a scored contact list the same day.

Try It Free

Try it free, cancel anytime.

Why I built this

I was buying lead lists like everyone else. Filter by job title, company size, industry. Export a CSV. Blast emails. And the reply rates told the story — under 1%. These people had no idea who I was and no reason to care. The only thing connecting us was a database that said they had the right title at the right size company.

Then I started paying attention to where my actual customers came from. Almost all of them had done one thing before buying: they'd engaged with content about the exact problem my product solves. They'd commented on an article. Asked a question in a forum. Replied to someone else's thread. They were already thinking about the problem — I just needed to find them.

The problem was doing that at scale. I couldn't manually browse articles and copy commenter names into a spreadsheet. So I built a system that does. It takes a topic, finds every article with an active comment section, extracts every commenter, scrapes their profile for company and title, and then runs five different email discovery methods to find their contact info. Each email gets a confidence score so I know which ones are real.

The difference was night and day. Instead of emailing strangers with the right job title, I was emailing people who just told the internet they care about exactly what I sell. The replies went from "who is this?" to "actually, we were just talking about this."

Common questions

The system currently focuses on LinkedIn articles (Pulse) since they have the richest commenter profiles — name, headline, company, and LinkedIn URL are all exposed. The platform also supports other article sources through configurable search operators. You're scanning for people who engage with content, not just people who exist in a database.

Every email found gets scored 0-100 based on two signals: does the person's name appear in the email address, and does their company domain match? A score of 100 means both matched from a reliable source — that email almost certainly belongs to that person at that company. A 70-80 means the email format matches known patterns at their company but wasn't directly confirmed. Below 50, treat it as a fallback. You choose your export threshold.

In order: (1) the commenter's own profile — some people list their email publicly. (2) Their company website — many company sites expose team member emails. (3) Google search — querying their name plus company for contact info. (4) Email format databases — if we know their company uses [email protected], we generate and score the guess. (5) Contact APIs — checking third-party databases for known email addresses. The waterfall runs each in sequence and scores every email found across all sources.

It depends on the topic and how actively people discuss it. A topic like "cold email strategy" with hundreds of articles might yield 200-400 unique commenters. Of those, typically 50-100 will have high-confidence emails (score 90+). Niche topics with fewer articles will produce fewer contacts, but the ones you get are more precisely targeted. Quality over quantity — these are people who demonstrated genuine interest.

Yes. Contacts export as contact lists with email, first name, last name, company name, and confidence score. You set the minimum score threshold for export. The contact lists integrate with the platform's email verification and outreach tools, or you can export as CSV for your own CRM.

Apollo and ZoomInfo give you people based on job title and company. That's demographic targeting — you know who they are but not what they care about right now. Comment scanning gives you behavioral intent: these people actively engaged with content about the topic your product addresses. A "VP Marketing" from Apollo might not be thinking about your problem today. A commenter on an article about your exact problem definitely is. Plus, the 5-source email waterfall often finds contacts those databases miss.

Right now, someone is commenting on an article about exactly what you sell. They just told you what they need.

Every day without comment scanning is a day of intent data going to whoever finds it first.