In just ONE HOUR I did this, with only my voice… this what GTM Engineering looks like in the future… 100% in Claude Code and with Apify ! What We Did: Review Enrichment & Classification Pipeline We built a scalable pipeline to pull and analyze Google Maps reviews for 9,732 ********* practices—every company with 150+ reviews plus all PLATINUM/GOLD tier prospects. Using Apify's Google Maps Reviews Scraper at Scale tier (128 concurrent actors), we collected 201,556 unique reviews across 9,423 locations in under 2 hours. Each review was then processed through Claude's Message Batches API for pain signal classification, categorizing reviews into six operational buckets: scheduling issues, communication problems, billing complaints, staff issues, legacy technology mentions, and growth constraints. The classification results revealed that detectable pain signals are sparse: only ~1.8% of the 161,379 processed reviews contained identifiable operational complaints (2,787 communication issues, 1,981 billing mentions, 1,930 staff problems, 1,248 scheduling complaints, 1,089 growth signals, and just 453 legacy tech mentions). This reinforces the validation findings—Google reviews aren't a reliable source for identifying "******* in pain." The signal-to-noise ratio is too low, and negative reviews often reflect one-off experiences rather than systemic operational dysfunction that would drive software purchasing decisions. Based on this analysis, we've assigned each of the 9,732 companies a recommended product (PRODUCT 1, PRODUCT 2, or PRODUCT 3) and a message hook tailored to their dominant signal—though 69% defaulted to the general value proposition since they lacked detectable pain. The actionable output: focus outreach on ***-change signals and ******* maturity rather than trying to infer pain from reviews. The data supports "you're evolving, here's infrastructure that scales" messaging over "we see you're struggling." This dog is how you’ll feel like when you start building this way…
We’re entering a new era of GTM from ChatGPT > Clay > Claude Code. Here’s how I see the change…
AI message personalization is A CANCER on sales and here’s what to do INSTEAD… Think about what just happened in this message… =============== “{{generic summarized fact about me that sounds like a robot wrote it}}” “Here’s what everyone does” “Here’s...
Here’s how I structure my GTM Engineering Systems in Claude Code and how you can too…

The video introduces a novel approach to go‑to‑market (GTM) engineering that leans heavily on Claude, an AI large‑language model, to automate the bulk of campaign creation and list origination. The presenter explains how, instead of repeatedly feeding the model with...
HOT TAKE: SDRs deserve the heated replies they get from prospects. Each week you see SDRs taking screenshots of replies from buyers that are like… “this person told me to jump off a cliff” The invariable response looks like this:...
Today I am pardoning all you turkeys that say Clay is expensive… Here’s how to NEVER PAY more than $800/mo for Clay. First off an enterprise yearly subscription to Clay with CRM integration is under $9k/year and your cost is...
You can't personalize your way out of a targeting problem. Focus on finding evidence of the problem, then just ask them if that evidence means they are struggling with X problem. This is called a PQS, pain qualified segment, and it will...
"Congrats on scaling Blueprint" <— NEVER try to scale a compliment and here's why… The whole purpose of a compliment is that it's from the heart, genuine. Also, the whole purpose of automation for sales is to open up conversations. But how...