About
About the Replaiy blog
This is a publication about what happens after someone accepts your connection request. Not lead lists and not subject lines, but the conversation itself: how fast you reply, what you say to a lukewarm "sure, send me info", and how a small team keeps hundreds of those threads alive at once.
Who this is for
Founders doing their own outbound, SDR and BDR teams working LinkedIn at volume, and the RevOps people who have to make the numbers add up afterwards. Most of our readers run somewhere between one and forty sending seats, use a sequencer such as Expandi, Lemlist, Apollo, Amplemarket or similar, and are stuck on the same bottleneck: enough conversations start, too few of them get worked properly.
If you are looking for growth-hack listicles or "10 LinkedIn hacks" content, this will not be a good fit. Posts here run long, include the numbers, and assume you already know what a sequence is.
What we cover
Everything is filed under one of four tracks, each with its own standing brief:
- AI Sales
- How AI agents are changing prospecting, qualification and the sales conversation itself, with the tradeoffs stated plainly.
- Playbooks
- Step-by-step systems for LinkedIn outbound: sequences, reply frameworks, objection handling and handoffs you can copy today.
- Benchmarks
- Reply rates, response times and conversion data from real outbound programs, so you can tell a good number from a vanity one.
- LinkedIn Outbound
- Everything specific to the LinkedIn channel: connection limits, inbox hygiene, deliverability and what still works in 2026.
Editorial standards
We publish fewer posts than most B2B blogs because each one has to clear the same six checks before it goes live. These are the rules we hold ourselves to. If you find a post that breaks one of them, tell us at [email protected] and we will correct it.
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01 Every number has a source and a sample size
When we publish a reply rate, a response time or a meeting-conversion figure, we say where it came from and how many accounts or messages sit behind it. Aggregate data from Replaiy workspaces is always anonymised and reported in ranges. If a number comes from a third party, it is linked. If we cannot source it, we do not publish it.
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02 Playbooks are tested before they are written
A sequence, reply framework or objection script only ships once it has been run against a real list, ours or a customer's, with permission. We describe the conditions it was tested under, including ICP, list size and seniority, because a framework that works for a 40-person SaaS list often falls apart at enterprise.
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03 We state the tradeoff, not just the upside
Automation has costs: restricted seats, generic-sounding threads, prospects who notice. Any tactic we recommend comes with the failure mode attached and the volume at which it stops working. If AI is the wrong answer for a step, the post says so.
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04 Named authors with relevant experience
Posts carry a byline, a role and an expertise list: no house pseudonyms and no unattributed content. Authors write inside the areas they have actually operated in, whether that is running an SDR team, building the models, or doing revenue operations.
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05 Updated in place, with the date shown
Outbound changes fast: platform limits move, deliverability shifts, and what worked last year gets burned out. We revise posts rather than letting them rot, and every revision updates the "Updated" date at the top of the article so you can judge how current the advice is.
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06 We are transparent about the product angle
Replaiy is a commercial product and this blog exists in part to explain the problem it solves. Product mentions are marked as such and never dressed up as a neutral recommendation. A post that only works if you buy something is a landing page, not an article, and we keep the two apart.
Corrections and updates
Substantive corrections are made in the body of the post and the updated date is bumped, so a reader can always tell the advice has moved. Typos and formatting fixes are made silently. If a platform limit or a benchmark changes enough to invalidate a recommendation, we revise the post rather than publishing a fresh one that competes with it.
What Replaiy is
Replaiy is the AI conversation layer for outbound sales. It handles your LinkedIn replies in your own voice to book more demos and calls.
In practice: you connect your LinkedIn inbox, Replaiy learns how you write from your own past threads, and it takes the first pass at every incoming reply: answering questions, handling the usual objections, qualifying against your criteria and proposing times. You keep control of when it hands a conversation back to a human. It runs on top of the sequencers you already use (Expandi, Lemlist, Apollo, Amplemarket) rather than replacing them, so your existing list building and outreach stay exactly where they are.
The blog came out of that work. Building an agent that has to hold a sales conversation means looking closely at what makes those conversations succeed or stall, and most of what we learn is more useful in public than in a changelog.
The outbound teardown, every other week
One email with a reply-rate benchmark, a sequence teardown and the LinkedIn changes worth caring about. No fluff, unsubscribe anytime.
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Stop losing deals in the LinkedIn inbox
Replaiy is the AI conversation layer for outbound sales. It handles your LinkedIn replies in your own voice to book more demos and calls.