adriansanthosh

How I got a 40% reply rate from just 115 outbound messages. Over the last 30 days, I ran a signal-based outbound experiment targeting 115 highly specific accounts. Not volume-driven. Relevance-driven. Here’s what happened: 115 targeted accounts reached 40% reply rate 48% LinkedIn connection acceptance rate 9 meetings booked 7 technical GTM Architecture built & Project convos 1 in-person meeting 4 inbound opportunities 2 warm referrals A meaningful part of this came from follow-ups. Some of the strongest replies happened on the second or third touch, not the first. But the interesting part wasn’t the numbers. It was the learning loop. Every reply improved the system. Every objection refined the ICP. Every rejection sharpened the messaging. Every follow-up improved timing. Instead of asking “how do I send more messages?” I kept asking: Am I targeting the right signals? Is this ICP actually real or assumed? Does the message create curiosity in 5 seconds? Is the follow-up adding new context or just repetition? Which steps can be removed entirely? That’s how I think about GTM Engineering. Not as outbound volume. But as a system that improves with every interaction. The goal isn’t to send more messages. The goal is to build a system that gets better after every message. Still iterating. Still building.

Hey everyone — I’m working on web search for AI agents and looking for free (or better) alternatives. I’ve tried CrawlAI, but I need more options. Does anyone have suggestions?

Hey everyone 👋 I'm Adrian, a GTM Engineer based in Chennai, India. I build signal-driven GTM systems that combine AI, automation, and data to turn market signals into pipeline. Over the past month, I've designed 7 end-to-end GTM architectures and built several production-ready projects, including: → Autonomous GTM Agent — an AI-powered GTM system with 30+ specialized agents and an MCP server that connects directly to tools like Apollo, HubSpot, Gmail, Slack, and more. It handles prospecting, enrichment, ICP qualification, personalized outreach, reply classification, and meeting booking. → Intent Radar — a signal-based GTM engine that combines hiring activity, technographic data, social signals, public pain points, and enrichment to identify and prioritize high-intent accounts. → RAWMATE — a B2B procurement marketplace I'm building from scratch, where I own the product, GTM, and growth. → A collection of open-source GTM tools, including CRM automations, geo-sales intelligence dashboards, WhatsApp automation, and AI-powered revenue workflows. I'm newer to Clay, but I'm already using it to build signal-based prospecting workflows and integrating it into larger AI-native GTM systems. Always happy to swap workflows, discuss GTM architecture, collaborate on interesting projects, or contribute to open-source GTM engineering. Portfolio: app.notion.com/p/GTM-Engineer… Looking forward to learning from everyone here and contributing back to the community.