How can I collect chatbot conversations into a spreadsheet by session ID and automatically add extracted names and emails to our CRM?
You don’t need to parse the chat yourself. Add a line to your prompt asking the model to capture name and email when they appear and output them directly. Then, in the workflow, map the model’s JSON response to a Google Sheet: create a row with columns for the session ID, the raw message, and the extracted name/email. Use the ‘Add Row’ action, select your spreadsheet, and map the fields from the node output (message, response, session ID) to the appropriate columns. This eliminates manual parsing and lets you store each chat session in the sheet automatically.
Yes, absolutely. Go to platform.openai.com, open the playground, create an assistant, and add a vector store. Upload your files to the vector store, then reference it in your prompts. It’s just a matter of consistency.
Yeah, absolutely — it's just a matter of time. We're already getting to the point where you can have really good, seemingly customized conversations with them, just using GPT-4.5 (the new model that just dropped about a week before this recording, and it's very good). I have no doubt that the next generation of models will let you simply say "hey, this is the business, here's some stuff they do, do it" — it'll automatically pick up the tone of voice, automatically pick up what you want it to do, and even ask you a clarifying question when you initialize it, like "do you want me to book appointments? What do you want me to do?" A lot of this is going to get done for you. When that happens, your point of leverage changes — it stops being about actually installing and implementing a chatbot with a bunch of agent features doing function calling or tool calling, and becomes about knowing where the tools are and the simplest, easiest way to deploy them for the customer. So yeah, we'll get there pretty soon — probably not for at least another year, maybe six months to a year — so there's still a lot of value in knowing how to do it manually right now.
Personalizing messages ties into enrichment. After you verify leads, you’ll have a CSV or Google Sheet of contacts. Dump that file into a Google Drive folder, set up a watcher that triggers a flow (using Make, N8N, or another no‑code platform). The flow reads each row and runs an enrichment automation powered by AI, adding relevant details to each contact. This adds a personal touch without manual effort. While there may be extra costs for the enrichment service, at $6,000 a month in revenue you could comfortably spend $500‑$1,000 on automation that saves you countless hours and makes the business run smoother.
It depends on your purpose – you don’t need a knowledge base for everything, but it’s useful for letting an AI answer business‑specific questions that would otherwise require a database query. For example, I would feed all publicly available information about me, my company (Left Click Software), and our major case studies into the system. I’d host the chatbot on a platform like Botpress or NLU and connect it directly to Claude via API. The system prompt would contain a simple template with a few conversation routes to keep answers constrained. All the data would be embedded in a vector store such as Pinecone, and Claude would query Pinecone for the top five to ten matches before responding. This is standard RAG: each incoming message triggers the system prompt, a Pinecone similarity search, injection of the results into the prompt, and then the answer. The key is to be specific – you can’t feed everything – and to consider security, since exposing internal data through a public model can leak sensitive information.