This is a customer support chatbot that help users to sell their products online. User will chat with on your website. It collect leads on your behalf gather detail in a google sheet. It will even create meeting add to your Google calendar. It is useful for you as an individual, an entrepreneur, as somebody delivery services or product.

I use my niece boutique as a reference.

Tools: Ai Agent, Ai chat model, memory, code tools, Google sheet and Google calendar. Ai Agent: as Ai agent you it a task, it will use several tools at it's disposal to respond and act based on the system prompt.

Ai chat model: respond to all the chats.

Memory: you give your agent memory so that it can remember everything read understand and interpret it.

Code tool: It return the days of the week.It gather information about people that are chatting even see them as potential buyers or not.

Google sheet: It record every sales.

Google calendar: You use Google calendar to book an appointment with your customers

Most inbound bottlenecks aren't caused by low lead volume—they happen during triage.

When inquiries roll into a Google Sheet or CRM, operations teams often waste 10–15 minutes per submission reading through messy paragraphs, deciphering budget mismatch, and manually deciding who gets an immediate call vs. a standard rate sheet.

Instead of adding a bulky CRM add-on, I documented a lightweight 3-step automation pipeline built inside Make:

Intake & Extraction: An active listener triggers immediately upon a new submission, extracting core variables: client scope, target deliverables, timeline, and stated budget.

Context-Aware Evaluation (Gemini 2.5 Flash): The payload passes through an AI evaluation node with strict guardrails:

Data Return & Clean Pipeline: Appends the structured classification directly back to the master operational tracker, keeping the sheet filterable and clean.

The outcome: The sales and operations team can filter the entire board by priority status in two clicks, cutting first-touch response time to minutes without manually reading every single submission.

I’ve documented the full scenario map, execution logic, and data schemas below.

Curious how other agency operators and automation leads here handle their lead enrichment layer—are you doing AI scoring at the form level, or later in the CRM pipeline?:

For the budget gate, I'd keep an unstated budget separate from one explicitly below your minimum. Does the Make route ask for clarification when the amount or currency is missing, so a potentially suitable lead doesn't fall into Cold by default?