Support customers with AI
Answer faster and deflect tickets without losing the human touch.
3 steps to get you moving, each with a resource worth your time and more waiting underneath
Think of this as a friendly starting line, not the last word. Each step gives you the gist, then a resource worth your time from founders who've actually done it. There's always more underneath, more questions and more resources, whenever you feel like digging in.
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1
Customer SupportWhy can AI now handle most support tickets, and should it?
The gist LLM agents can now read your help docs, past conversations, and backend data, which is why Intercom's Fin resolves around 56% of conversations on average and Decagon deployments at companies like Bilt report 75% resolution. The 'should' is more nuanced: Klarna famously replaced the work of 700 agents with AI, then publicly rehired humans in 2025 after quality complaints. The emerging consensus is to let AI own the repetitive tier while guaranteeing customers a real human path for complex or emotional cases.
Intercom's decisive bet on AI Kyle Poyar (Growth Unhinged) The clearest outside teardown of how betting the company on an AI support agent took Fin from $1M to $100M+ ARR. -
2
CRM & Sales CallsHow do founders use AI notetakers and call analysis (Granola, Fireflies, Gong-style tools) to close more deals?
The gist AI notetakers remove the oldest tradeoff in founder-led sales: you can be fully present in the conversation while the tool captures every objection, buying signal, and promised next step. The compounding win is what happens after the call, since the transcript becomes fuel for sharp follow-ups written in the buyer's own words, CRM updates, and pattern-spotting across dozens of calls. Gong's research on millions of calls shows the basics that actually move win rates, like listening more than you talk and keeping the conversation interactive.
AI notetaker for sales calls: how to create personalized follow-ups that close deals Granola Shows the exact capture, process, draft workflow that turns a call transcript into a follow-up written in the buyer's own words. -
3
Workflows & AutomationWhat's the difference between automation (Zapier, n8n, Make) and AI agents?
The gist Tools like Zapier, Make, and n8n run fixed recipes you design in advance: when X happens, do Y, then Z, the same steps every single time, which makes them cheap and reliable. An AI agent adds a language model that can read the situation, make decisions, and choose its own next steps, which makes it flexible but also slower, pricier, and less predictable. Most founders actually need a plain workflow with one smart AI step inside it (e.g. 'summarize this email' or 'draft a reply'); save true agents for tasks that genuinely require judgment.
AI Agents, Clearly Explained Jeff Su The clearest 10-minute, jargon-free walkthrough of the LLM → AI workflow → AI agent ladder, with real examples a non-technical founder can follow.
