𝐀𝐩𝐩𝐬 𝐒𝐜𝐫𝐢𝐩𝐭 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐟𝐨𝐫 𝐆𝐨𝐨𝐠𝐥𝐞 𝐒𝐡𝐞𝐞𝐭𝐬 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬
I developed a custom Google Sheets dashboard powered by Google Apps Script to automate data processing, reporting, and business insights.
The system transforms raw spreadsheet data into a structured, interactive dashboard where users can monitor key metrics, track performance, and generate reports without repetitive manual work.
Using Google Apps Script, I automated data updates, calculations, filtering, formatting, and dashboard actions while keeping the entire solution inside the Google Workspace ecosystem.
Key features included:
Interactive Google Sheets dashboard
Automated data processing and calculations
Custom Apps Script functions and workflows
Dynamic KPI and performance tracking
Automated reporting and data updates
Smart filtering and structured data management
Reduced manual spreadsheet operations
Scalable and easy-to-manage workflow
The final solution provides a simple interface for users while powerful automation runs behind the scenes, turning Google Sheets into a practical business reporting and operations tool.
Technologies: Google Sheets, Google Apps Script, JavaScript, Google Workspace, Automation
Business data often lives across payment, analytics, CRM, advertising, and inventory platforms. Metrik brings those signals together in one dashboard built for startups and small businesses.
See revenue, orders, customers, and business health at a glance. Compare advertising spend and ROAS across channels, explore visual KPI cards, and use smart alerts to spot unusual changes and potential risks.
The interface pairs a clean layout and rounded components with soft gradients, colorful analytics, and clear visual hierarchy, helping make business performance easier to follow.
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?
Proud to officially add the AWS Certified Machine Learning Engineer - Associate certification to my toolkit!
Building robust machine learning pipelines and scaling AI workloads securely on the cloud has always been a key focus of mine. Earning this certification has been a great deep-dive into mastering AWS ML infrastructure, MLOps best practices, and production-grade architectures.
Ready to bring these advanced capabilities to upcoming data science and engineering projects! Let's build something impactful. 💡💪
Congratulations on earning the AWS Machine Learning Engineer certification, Hazem khaled! Robust pipelines and cloud automation make all the difference for production-grade AI systems.