The Missing Piece in AI-Powered LinkedIn Outreach
Everyone has access to the same AI models now. But the quality of sales outreach still varies massively.
The difference is usually context.
That’s why we’ve built https://networkhq.io and reached 6K in MRR.
It finds relevant HIGH INTENT LEADS to reach out on Linkedin WITHOUT TOUCHING YOUR LINKEDIN ACCOUNT. You don’t have to connect your account, or run any automation from your profile just to start sourcing leads.
NetworkHQ continuously looks for:
- Companies that match your ICP
- People showing relevant buying signals across the WEB
- The right decision-makers inside those companies
- Recent activity that gives you a reason to reach out
- Useful context for writing a message that doesn’t feel generic
Because asking AI to write a message with just a name, title and company is not enough.
It needs to understand:
- Why this company?
- Why this person?
- Why now?
That’s the real value of AI in LinkedIn sales. Not generating more generic messages, but finding the right people and giving the AI enough context to write something relevant.
We’ve opened up a 14-day free trial for NetworkHQ.
- No credit card required.
- No credit-based limits.
- No artificial feature restrictions.
You can use it to find high intent leads on autopilot, add more buying signals and start building a solid pipeline.
Totally with you on this. Context is what separates a personalized message from one that just looks personalized. Curious, what's one buying signal that's worked much better than you expected?
At $6K MRR, I’d make disqualification precision the primary quality gate before reply rate: of the leads NetworkHQ surfaces, how many does a seller reject before sending, and why? Signal age, wrong buyer role, or “building in-house” are different failures. Accepted lead → real conversation is a cleaner measure of context quality than drafts generated or messages sent.
The "why now" piece is the one I'd want to see fail gracefully, because signal-to-outreach systems tend to break the same way: the signal itself gets misread. A company posting a job for a role adjacent to your ICP isn't the same as them actively evaluating tools like yours — it could just as easily mean they're building it in-house. If NetworkHQ is confident enough to draft a because-of-this-signal opening line, what happens when the signal turns out to be a false positive? Does the message stay generic-safe, or does it commit to a specific claim that ends up being wrong in front of the exact person you wanted to impress?
Really like this approach. Curious—how do you prioritize and score different buying signals? For example, would a recent funding round outweigh someone engaging with competitor content, or is it a combination of signals? Congrats on the 6K MRR!
The interesting shift is from AI-generated outreach to AI-generated relevance. I'd keep validating whether customers are paying for more messages or for a reliable system that identifies the right moment and context before outreach begins.
It's not an OR game.
Customers pay for a reliable system that identifies high intent signals based leads AND then a structure that can craft compelling messages based on the signals - since not all signals are the same - it should know what to ignore and what to incorporate and think from the message receiver's point of view as to what would matter to them.
That's exactly why I didn't frame it as an either/or.
Reading your reply gave me one thought about the relationship between those two layers. I don't think I could explain it properly in a thread because it really depends on how you're thinking about your product rather than AI outreach in general.
If you're interested, what's the best email to reach you on?