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Getting the names is the easy part. Sai searches a company's people, pulls the executives and partners, and marks which ones are already 1st or 2nd degree connections of yours, with the mutual contact who can make the intro.
The recording is a real session. The sheet on the right is what it produced.
Sai opens each profile, pulls the signal, and writes the row, live, in a real browser.
Eight columns, sorted by score, with a source link behind every claim.
A LinkedIn account you're already signed into, and the name of the company you're targeting.
A table of executives and senior people at that company, each with their title, profile link, and connection degree — sorted so the people you can actually reach are at the top, with the mutual connection who can introduce you.
Around 5 minutes for a mid-sized company.
Re-run before each round of outreach, or monthly on your key accounts to catch new hires and departures.
Open the company's LinkedIn page and click the People tab. That view lists everyone who has that company as their current employer, and you can narrow it by job title, seniority, location, or function.
Search "VP," "Director," "Partner," or "Chief" in the title filter and you'll have the leadership team in about two minutes. It's free, it doesn't require Sales Navigator, and for most companies it's reasonably complete because the data comes from people maintaining their own profiles.
That's the whole method. It's worth being honest that this part isn't hard.
Because the list was never the bottleneck.
Pull thirty executives off a company page and you have thirty names you have no reason to be talking to. Cold outreach to that list performs the way cold outreach always performs. The names are freely available to you, to your competitor, and to everyone else selling into that account — which is another way of saying the list has no value, because value comes from scarcity and nothing about a public employee directory is scarce.
What's scarce is a reason to be in someone's inbox. And the strongest one available is that you already have a connection in common.
That information exists on LinkedIn. It's just not in the view you were looking at. The People tab shows you who works there. It doesn't organize them by how close they are to you.
One tells you who exists. The other tells you where to start.
Take a real example. You pull 30 senior people at a target account. Ranked by title, the CRO is at the top, so that's who you email, and that email performs like every other cold email to a CRO.
Now rank the same 30 people by connection degree instead. Four of them turn out to be 2nd degree — you share a mutual contact with each. One is a former colleague of someone on your own team. Suddenly there are five conversations available to you that don't start cold, and the right first move isn't an email to the CRO at all.
Same 30 people. Completely different plan. The only thing that changed is that the list got sorted by something other than seniority.
Because at a law firm, VC, consultancy, or agency, there is no single decision maker to find.
Partners run their own practices, own their own client relationships, and make their own purchasing calls. Finding "the managing partner" gets you someone whose approval isn't required for the deal you're working on. The person who matters is the partner running the specific practice area your product touches — and identifying which one that is takes reading their profile, not filtering by title.
This is also where warm paths matter most. Partners are heavily targeted, so their inbound is aggressively filtered, and a referral from someone they trust is often the only thing that gets read. On a list of forty partners, the two you share a mutual connection with are worth more than the other thirty-eight put together.
A table, sorted by reachability rather than by seniority.
You give it a company name and it searches that company's people on LinkedIn, opens the profiles of the executives and senior staff, and builds a row for each one: name, title, connection degree, and profile URL. For 2nd degree contacts it also captures the mutual connection you share, which is the piece that turns a name into an actual next step.
Because it's reading LinkedIn the way you would — logged in as you, seeing what you'd see — the connection degrees are yours specifically. That's the part no exported dataset can contain. A scraped CSV of a company's executives is the same file for every buyer of that dataset. This table is only true for you.
Sai works in a visible browser while it runs, so you can watch the searches and profile visits happen and stop it at any point.
Scrapers optimize for volume and contact details. This optimizes for a way in.
An export tool will give you more rows and often an email address, which is genuinely useful if your motion is high-volume outbound. What it can't give you is your own relationship graph, because that data only exists from inside your logged-in account. Degree, mutual connections, shared history — none of it is in the dataset being sold to you.
There's also a durability difference worth knowing. Bulk scraping violates LinkedIn's terms of service, and accounts doing it at volume get restricted. Sai runs searches at human pace in a real session, which is why it's a five-minute task rather than a ten-thousand-row export. That's a deliberate tradeoff, not a limitation we're apologizing for — the output is a shortlist you'll act on this week, not a database you'll never open.
Work the table from the top, not from the org chart.
Start with the 1st degree contacts, because those are people you can message directly today with no intermediary. Then go to the 2nd degree rows and look at the mutual connection column — for each one, decide whether that mutual is someone who'd genuinely vouch for you. If yes, the ask goes to them, not to the target.
The remaining names are your cold list, and they're worth keeping. Just work them last and expect them to behave like a cold list, because that's what they are.
