articuler.ai - Describe your goal. Meet the right professional.

@novamaker01 Glad that landed. In-Event Match is the part we think gets least talked about and matters most — it's the only place where the cost of meeting the wrong person gets paid in real-world hours, not just an unread email. If you've got a conference coming up, worth signing up an event slot.

@novamaker01 Thank you Felix! Event matchmaking comming next, feel free to connect and I will get you our first hand information!

@haoran_fok Henry, yes — strongly yes 🙏 BD is one of our top use cases, because every partnership is ultimately people-to-people. The deal closes between two humans.

Real examples from our users:

· Consumer electronics brand → finding EU distributors who've ranged competing audio products

· CRM SaaS → mapping VPs of RevOps at companies that just switched off Salesforce

· Forklift manufacturer → finding warehouse automation integrators with Tier-1 deployments

None findable on LinkedIn keyword search. Partnership BD lives in the signal layer, not the title layer. What angle are you working on?

(BizCard looks slick 👌)

@venier Giacomo, thanks 🙏 Yes — recruiting is one of our strongest use cases, with one specific philosophy: good hiring isn't finding a list, it's finding people whose background aligns with what you're building.

"Growth engineer" returns 50,000 LinkedIn results. For Snaply, you'd want something like "growth engineers who've shipped voice/audio products or written about real-time AI infra." LinkedIn can't filter for that. Public footprints (tweets, blogs, GitHub) can. Happy to run it for you.

Also worth flagging: a real chunk of Articuler's users are young professionals actively looking to join startups exactly like Snaply — they're already on our platform describing what they want. We've been thinking about building a dedicated startup-hiring directory that matches founders like you with these candidates. Snaply feels like the right launch partner for this — are you in?

@venier Thank you for your comment, Giacomo. Yes, just as Jason mentioned, we do exactly what you asked!

We find you the right person for your next hire, but we can also do matching on the other side and connect potential employees to you by adding founders and job seekers into the same directory.

@yankun_zhao 🚀🚀You next financing round is on us!🚀🚀

@yankun_zhao Hey Yankun, you get the gist!

@cruise_chen We're deliberately not a batch tool. Articuler drafts a separate message per person based on that specific person's profile, recent activity, and your overlap with them — sending the same message to a list isn't really the workflow. Today you can use the drafts in email, LinkedIn DMs, or anywhere else you paste text. The tradeoff is real: you send fewer messages, but reply rate is the multiplier (we see ~8x over generic blast). If your headache is prep time per email rather than send mechanics, you're in the right place. If it's pure volume, we're probably not the right fit.

@cruise_chen Right now, articular.ai can connect directly to your Gmail. However, we can also craft dedicated messages for you to copy and paste on other social media platforms for outreach (for example: LinkedIn, Twitter, etc.).

@jojo_li JoJo, great question 🙏 Short answer: both — but finding and pitching clients is actually where Articuler shines hardest.

Two sides of the same engine:

· Finding — instead of "VP of Marketing at SaaS companies," you can run "VPs of Marketing at mid-market SaaS companies who recently mentioned localization pain or hired international growth roles." That's signal, not title — and signal is what tells you who's actually ready to buy.

· Pitching — this is where Playbook earns its keep. For every prospect, we read their entire public footprint and tell you what they care about, what they've publicly criticized, what kind of pitches they've responded well to. Think of it as the briefing you'd get from a friend who actually knows them — the friend most BD reps wish they had before every cold call.

Then we draft the first email anchored in shared context, not a template. 15% reply rate, 8x cold outreach.

What does your ICP look like? Happy to run a sample search — would love to see Articuler land a Vozo deal 👌

@madalina_barbu ❤️❤️❤️

@madalina_barbu Thank you Madalina!!!

@fabian_exner Fabian, this is exactly the design problem we obsess over 🙏

we don't use keyword matching at all. We embed each person's full public footprint, directly into a semantic vector. Matching happens in vector space, not in keyword space.

So the "profile doesn't use expected keywords" failure mode doesn't really apply. Two people writing about the same idea with different vocabulary — "climate" vs "energy transition", "distributed systems" vs "large-scale infrastructure" — end up close in the embedding space because the model captures meaning, not surface words.

We don't search profiles, we match meanings. That's the difference between matchmaking and search.

Caveat: there's still real work in deciding which signals to weight up (behavior vs declaration, recent vs old activity). But the "missing keyword" failure is mostly a keyword-system problem, not a vector-system problem.

Right thing to push on 🚀

@le_ng_c_dan_nhi ❤️

@thekrew Really appreciate this — and you're touching on exactly the right problem.

Volume was never the bottleneck. Relevance is.

The 15% reply rate comes from our Playbook feature, before you even press the "send" button. Before any outreach, Articuler.ai reads both sides — your background and theirs — and finds the actual shared ground between you. That's what makes the first message land. It's not a template with a mail-merged company name. It's context that could only come from understanding both people. We are like that "guy who knows the other guy".

On stale data — great question. Public profiles do go stale, and we're aware that's a real edge case. Right now we mitigate this by pulling from multiple public sources rather than relying on a single profile, so even if someone's LinkedIn is outdated, their recent activity elsewhere can fill the gap. That said, this is an area we're actively improving — the goal is to flag confidence levels on match data so you know when context is fresh vs. inferred. You can truly see the degree of matching between you and the target individuals on this list.