The future of GTM is here. nRev is a GTM wizard, trained on 10,000+ deployed marketing and sales engines. It consults based on what's working, builds and deploys automations, simply by having a conversation with it.
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Promoted
“Tried this today pretty interesting so far. Planning to use it for LinkedIn GTM for our product to drive B2B clients. Curious to see how well it handles platform-specific nuances though LinkedIn outbound and content strategy can get very context-heavy (ICP, tone, sequencing, etc.). If it can go beyond generic playbooks and actually adapt to that level, this could be a strong tool.
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Maker
@shivang_kamboj2 the fact that you're leading with this thought itself is a step in the right direction.
And of course, every message you send out can have every possible nuance like
ICP: A simple gatekeeper to ensure you don't waste your precious limits.
Tonality: Can be curated to the last detail or can be simply sourced from your past conversations ;)
Sequencing: Complete flexibility, you can space your messages based on your schedule and we ensure you stay adherent to the limits.
Personalisation: Anything from a generic signal to a person's interests driven from emerging patterns from their historical reactions can drive a message.
Are u only listening to LinkedIn and Reddit, or can it pick up signals from other public sources like X, Instagram, SEC filings, government data, news?
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Maker
@honzamartinek indeed. We have a suite of AI models that can access almost any of the sources you've mentioned to get you precise information on demand.
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Hi everyone, Bhavesh here!
I'm a Founding Engineer at nRev. My focus has been building the underlying engine that powers everything the team have shared. While Nikhil and Daksh were perfecting the AI "brains," I spent my last year building the "body"—the platform that ensures those agents have a solid place to work.
In GTM, "cool" doesn't matter if it isn't reliable. You can't run a $50M company on a system that "mostly" works. I've spent the last year obsessing over the platform's plumbing: ensuring that when an agent triggers a workflow, the data flows perfectly from the CLI or workflow to your CRM without a hitch.
We built nRev so you could stop being a "tool integrator" and start being a "GTM Architect."
We've taken the entire burden of managing vendors, fallbacks, and execution logic and baked it into the platform core.
If you've ever had a workflow break in the middle of a massive campaign, you know my pain. I'd love to know what's the most "fragile" part of your current GTM setup?
Report
How is this different from Clay? Genuinely curious. Sounds like a different category but want to understand the line.
Multiple angles to it. But the most fundamental one is table native vs workflow native. Every workflow block is a dataset of it's own. 1. Search for a 100 companies in block 1. Find relevant people in block 2. It automatically becomes a 500 person dataset.
Now try enriching emails. If found you can build a sequencing flow. If not, you can route to a linkedin warmup.
What's more we don't charge you for API calls or actions :P
Report
Hey everyone, Amit here, Founding Engineer at nRev.
Huge day for us. This has been a deep, hands-on build - taking GTM from a pile of stitched tools to something you can actually reason about, modify, and scale without friction.
My focus has been on making the system behave predictably under real-world complexity. GTM workflows aren’t static APIs fail, data is messy, and edge cases show up at scale.
We designed the platform to handle this natively: retries, fallbacks, state management, and observability are not add-ons, they’re core.
The goal was simple: you describe intent, the system figures out execution - reliably.
If you’ve ever had a campaign break because one integration failed or spent hours debugging a workflow across tools, I’d love to hear where things usually fall apart for you.
Report
💎 Pixel perfection
I've been using nRev AI for quite some time, and one thing that really stands out is how simple it is to build workflows.
For a non-tech like me, it's a good thing.
I describe in plain English what I want to build, and I sit back. I tested it so many times using my LinkedIn, and I was really amazed by the results.
I also love the visibility nRev Ai gives. You can track each workflow, its execution time, and even see how many credits you're using. This helps you plan better.
I think, overall, the level of transparency is really helpful.
Will this work for reactivating dead deals? Pulling closed-lost from the last 12 months and re-engaging only the ones showing fresh buying signals since
“Tried this today pretty interesting so far. Planning to use it for LinkedIn GTM for our product to drive B2B clients. Curious to see how well it handles platform-specific nuances though LinkedIn outbound and content strategy can get very context-heavy (ICP, tone, sequencing, etc.). If it can go beyond generic playbooks and actually adapt to that level, this could be a strong tool.
@shivang_kamboj2 the fact that you're leading with this thought itself is a step in the right direction.
And of course, every message you send out can have every possible nuance like
ICP: A simple gatekeeper to ensure you don't waste your precious limits.
Tonality: Can be curated to the last detail or can be simply sourced from your past conversations ;)
Sequencing: Complete flexibility, you can space your messages based on your schedule and we ensure you stay adherent to the limits.
Personalisation: Anything from a generic signal to a person's interests driven from emerging patterns from their historical reactions can drive a message.
Happy to brainstorm and showcase how we approach it: https://cal.com/aradhya-shandilya-oaznsv/15min
Are u only listening to LinkedIn and Reddit, or can it pick up signals from other public sources like X, Instagram, SEC filings, government data, news?
@honzamartinek indeed. We have a suite of AI models that can access almost any of the sources you've mentioned to get you precise information on demand.
Hi everyone, Bhavesh here!
I'm a Founding Engineer at nRev.
My focus has been building the underlying engine that powers everything the team have shared.
While Nikhil and Daksh were perfecting the AI "brains," I spent my last year building the "body"—the platform that ensures those agents have a solid place to work.
In GTM, "cool" doesn't matter if it isn't reliable. You can't run a $50M company on a system that "mostly" works. I've spent the last year obsessing over the platform's plumbing: ensuring that when an agent triggers a workflow, the data flows perfectly from the CLI or workflow to your CRM without a hitch.
We built nRev so you could stop being a "tool integrator" and start being a "GTM Architect."
We've taken the entire burden of managing vendors, fallbacks, and execution logic and baked it into the platform core.
If you've ever had a workflow break in the middle of a massive campaign, you know my pain. I'd love to know what's the most "fragile" part of your current GTM setup?
How is this different from Clay? Genuinely curious. Sounds like a different category but want to understand the line.
@theuiface
Multiple angles to it. But the most fundamental one is table native vs workflow native. Every workflow block is a dataset of it's own.
1. Search for a 100 companies in block 1. Find relevant people in block 2. It automatically becomes a 500 person dataset.
Now try enriching emails. If found you can build a sequencing flow. If not, you can route to a linkedin warmup.
What's more we don't charge you for API calls or actions :P
Hey everyone, Amit here, Founding Engineer at nRev.
Huge day for us. This has been a deep, hands-on build - taking GTM from a pile of stitched tools to something you can actually reason about, modify, and scale without friction.
My focus has been on making the system behave predictably under real-world complexity. GTM workflows aren’t static APIs fail, data is messy, and edge cases show up at scale.
We designed the platform to handle this natively: retries, fallbacks, state management, and observability are not add-ons, they’re core.
The goal was simple: you describe intent, the system figures out execution - reliably.
If you’ve ever had a campaign break because one integration failed or spent hours debugging a workflow across tools, I’d love to hear where things usually fall apart for you.
I've been using nRev AI for quite some time, and one thing that really stands out is how simple it is to build workflows.
For a non-tech like me, it's a good thing.
I describe in plain English what I want to build, and I sit back. I tested it so many times using my LinkedIn, and I was really amazed by the results.
I also love the visibility nRev Ai gives. You can track each workflow, its execution time, and even see how many credits you're using. This helps you plan better.
I think, overall, the level of transparency is really helpful.
Anyway, that's just me!
Congrats on the launch! Excited to see how it evolves. @sayanta_ghosh @jaypurohit09 @aradhya_shandilya
@jean_claude_samba_samba
Thank you so much. Would love to hear out use cases and brainstorm !
ASI:One
Will this work for reactivating dead deals? Pulling closed-lost from the last 12 months and re-engaging only the ones showing fresh buying signals since
@rajashekar_vennavelli Absolutely. You can gather all possible deal context and
Find out moved out and recently joined buyers
Personalise in any shape or form
And re-engage. Happy to brainstorm