Last post of launch week. GTM teams put real effort into building their @n8n_io and @make_hq workflows. They deserve clear visibility into how those workflows are actually performing. That is what @iqpipeapp is built around. We are early and learning a great deal from the teams already using it. If you work in RevOps or GTM engineering and this resonates, follow along. Feature deep-dives start Monday. And if there is a question about your stack you have never been able to answer easily, share it below. #GTM #RevOps #n8n #MakeCom

First week of @iqpipeapp in the world. The thing that stayed with us from early calls is watching someone ask their AI a real question about their pipeline and get an answer grounded in what is actually happening in their workflows. That is a quiet moment, but it is the one we spent months building toward. Still early. Still a lot to learn. Grateful to everyone who took the time to look and share their thoughts. #StartupLife #GTM #AIforSales

One week since @iqpipeapp went live. A few things we have heard from early teams: One team found a workflow misfiring for three weeks. They had been reviewing their messaging during that time. Another found contacts that had enriched but never entered a sequence. No visibility into that until now. The common thread: less about catching failures, more about finally seeing clearly what was happening across workflows they had already built. #RevOps #GTMEngineering #n8n

Five days of sharing the thinking. Today iqpipe is live and kicking. What we feel most settled about is that the problem is real. We have heard it clearly from enough people in enough different contexts. What we are still figuring out is how to serve it best. That is what early feedback shapes. If any of this week's posts resonated, we would genuinely welcome your reaction. Critical feedback is as useful as positive at this stage. #GTM #StartupLife #RevOps

This week we shared the thinking behind what we have been building. Today we want to introduce it properly. iqpipe mirrors your existing @n8n_io workflows and @make_hq scenarios, every app, module, and step you have already configured, and builds a live intelligence layer on top. Real-time workflow health. Full lead journeys. Funnel visibility end to end. And all of it available to your AI via the iqpipe MCP. We are live - iqpipe.com/signup #GTM #RevOps #n8n #MakeCom

Early on we tested an AI assistant and asked which of our sequences was performing best. It gave a well-structured answer with no connection to our actual situation. It had no idea which workflows we ran or what our pipeline looked like. Not the AI's fault. It had no context. That shifted how we thought about iqpipe. Less a monitoring tool, more a context layer that gives AI what it needs to be genuinely useful for GTM decisions. #StartupLife #AIforSales #GTM

A version of this comes up in almost every GTM conversation we have. Someone asks what happened to a set of leads from a campaign a few weeks back. The information exists across the workflows. Pulling it together takes most of an afternoon. We spoke with a lot of RevOps people before building iqpipe. This came up more than almost anything else. The data exists. Getting to it quickly is the hard part. That is what the correlation layer is built around. #RevOps #GTM #SalesOps

Every workflow in your @n8n_io or @make_hq setup is generating data. Enrichment events, sequence steps, handoffs between apps. The information to answer most pipeline questions is already in your stack. iqpipe mirrors your existing workflows and makes that data accessible in one place, attributed back to individual leads across every step. The data was always there. We just made it easier to use. #RevOps #GTMEngineering #n8n

Before building iqpipe we ran our own outreach stack. One quarter the results felt softer than expected. We reviewed the obvious things. Messaging, sequence structure, targeting. Eventually found it: a single workflow node silently failing on contacts sourced from LinkedIn. The issue was at a handoff between two tools. Everything around it looked normal. Had been going on for a couple of weeks. That experience shaped a lot of what we built into iqpipe. #StartupLife #GTM #SalesOps

More tools means more capability. It also means more handoff points, and more places where visibility can quietly drop off. Each tool reports on its own activity. None of them report on what happens at the seam between them. When something goes wrong at a handoff, it often doesn't surface anywhere obvious. It shows up later, downstream, as quieter results. @iqpipeapp watches that connection layer so your team doesn't have to piece it together manually. #GTMEngineering #RevOps #MakeCom

When we first described what would become iqpipe, people said: that happens sometimes, it comes with running a complex stack. We understood that. But every other function has some form of monitoring. GTM tends to find out something changed when a person notices the downstream effect, sometimes weeks later. That felt like a gap worth closing. @iqpipeapp is our attempt at it. Following along this week would mean a lot. #GTM #SalesOps #StartupLife

Something we noticed working with GTM teams: Most outreach issues surface gradually. A softer week, a sequence that quietly underperforms. Often the underlying issue started days earlier, at a handoff point between workflows that none of the individual tools could see. That gap is what iqpipe was built around. Sharing the thinking behind it this week. #GTMEngineering #RevOps #n8n

What GTM stack you building today?

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Hello. We are iqpipe.com We built a GTM context layer for teams running outreach on n8n and Make. Here is how it works. iqpipe mirrors your existing workflows and scenarios, including every app, module, and step inside them. We read the structure you have already built and create a live intelligence layer on top of it. That means we can tell you how each workflow is performing, which steps are handling events as expected, where leads are moving through and where they are stalling, and what has changed in behaviour over time. All of that context is also made available to AI via MCP. So when you ask your AI assistant a question about your pipeline, it answers from what is actually happening in your workflows, not from a generic understanding of your stack. We do not ask you to rebuild anything or connect tools individually. We work from the workflows you already have. We are live, we are early, and we are looking for teams who want to explore this with us. Good to be here. #GTMEngineering #RevOps #n8n #MakeCom #AIforSales