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I built an expense tracker that works inside WhatsApp. I send my expense as a message, n8n reads it and saves it in Google Sheets. Then I get a confirmation reply on WhatsApp. No app and no manual entry.
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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:
Evaluates budget against minimum agency criteria.
Assigns a triage score: Hot (immediate priority), Warm (nurture), or Cold (disqualified/template reply).
Generates a strict 15-word operational summary—no long paragraphs.
Determines an automated next step (e.g., routing to an account lead or flagging for asynchronous follow-up).
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?
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
Email Triage Automation
The Challenge
Growing companies often struggle with overflowing shared inboxes (sales@, support@, info@). Manual sorting, categorizing, and assigning emails to different departments causes response delays. Even worse, high-priority issues—such as urgent billing complaints or system failures—frequently get buried in the noise, leading to missed deadlines and unhappy clients.
The Solution & Architecture
I engineered an enterprise-grade inbox management and automation architecture using n8n, Gmail API, and OpenAI.
The system workflow functions as follows:
Ingestion & AI Classification: The workflow triggers instantly upon receiving a new message (Gmail Trigger), sanitizes the payload, and passes the text to an OpenAI Chat Model for sentiment analysis and intent classification.
Smart Categorization & Labeling: Based on the AI's output, a routing node assigns the email to the correct department (Finance, Sales, Technical Support, Partnerships) and automatically applies matching Gmail labels in real time.
Priority Escalation Routing: If the system flags an email as high-priority, it immediately triggers a secondary logic line. This packages the case details and shoots an urgent alert card directly into a dedicated Discord channel, complete with a summary and recommended actions.
AI-Assisted Contextual Response: Simultaneously, the system evaluates if an instant acknowledgement is needed, generates a contextual, brand-aligned draft reply using LLMs, and sends it directly within the original Gmail thread to reassure the client.
Business Results
Zero Sorting Overhead: Fully eliminated the manual labor required to filter, read, and forward inbound corporate emails.
Accelerated Escalation: Critical, high-priority issues (like duplicate billing charges) are delivered to managers' chat screens within seconds.
Improved Client Retention: Automated contextual auto-replies ensure clients receive an immediate confirmation that their issue is prioritized and routed to the correct desk.
I'd test calm messages about a service outage alongside angry messages about a routine request, so sentiment doesn't become a proxy for urgency. When the model is unsure of the department or priority, does the email go to a human review queue rather than being silently assigned?