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BChanel automated Amazon FBA shipment tracking across 40-60 inbound shipments by connecting Amazon SP-API data to a live Google Sheets dashboard with automated delay alerts.
Before the project, the logistics team manually checked Amazon Seller Central every morning across multiple brands, compared shipment status against expected arrival dates, and flagged possible delays by hand. As shipment volume grew, the manual process became difficult to manage consistently.
The result was 40-60 shipments tracked automatically every day, delay detection 7+ days earlier than the manual process, and zero manual Amazon Seller Central checks required.
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I'm building MorningAI — an AI automation platform designed to help businesses save time and work smarter.
I'm deciding on the website's visual direction, and I'd love your opinion!
🅰️ Design A: Light, clean & professional
🅱️ Design B: Dark, futuristic & premium
Which design should I choose for MorningAI — A or B?
Drop your choice in the comments! 👇
4 voted
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3 voted
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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?
Connecting order intake to the vendor workflow.
This TechFoundry IMS demo shows a purchase order feeding a vendor order, followed by date checks, vendor notification, and planning updates. A practical example of the AI and automation workflows we build into business software.
Watch the vendor order demo:
https://www.youtube.com/watch?v=vASYJhdLpZw
#AI #Automation #SoftwareDevelopment #Procurement