Why I'm Sharing My Actual Running Scenarios
People ask me all the time: "What automations do you actually use? Not theory—what's actually running right now?" I thought I'd just show you. Six scenarios. Real module counts. Real mistakes I made and fixed. No fluff.
I'm sharing these because the internet is full of "automation idea" blog posts that are purely hypothetical. But automations live or die based on whether they survive the first week, whether they handle edge cases, and whether they're worth maintaining. These six have all passed that test.
Scenario 1: Lead Capture to Welcome Email (8 modules)
This is my workhorse. Someone fills out a form on my website. That webhook fires, and Make.com takes it from there.
The flow: Webhook trigger receives form data, Google Sheets lookup (checking if email already exists), if new then send to Airtable base, parallel send welcome email via Gmail and log to CRM, tag contact with source (blog vs. product page).
What I learned building this: You need the Sheets lookup to prevent duplicate emails. I went live without it. Got three people the same welcome email. Not a disaster, but annoying. Now it's the first logic check after the webhook.
The parallel branches (email plus CRM log) were my second lesson. I originally had them sequential. Total execution time was 4-5 seconds. Now it's under 1.2 seconds because those happen simultaneously. If you're sending email AND updating a database, do them in parallel.
Cost: Free tier (1,000 ops/month easily covers this with 15-20 form submissions).
Scenario 2: Content Draft to Multi-Platform Publish (11 modules)
I write a blog post in Google Docs. When I move it to a folder called "Ready to Publish," Make.com catches that signal and publishes it to LinkedIn, Twitter, and my blog CMS automatically.
The flow: Google Drive trigger fires when file moves to "Ready" folder, read Google Doc content, send webhook to blog CMS, parallel create LinkedIn post (headline plus excerpt) and create Twitter thread (break content into tweets), conditional check for featured image and attach to each platform.
This one surprised me with complexity. I thought it'd be five modules. It's eleven because content formatting differs wildly between platforms. LinkedIn has a character limit. Twitter has different pacing. My CMS expects specific metadata.
What I learned: Don't try to send the same formatted content to every platform. Make.com's text functions let you truncate, re-format, and rebuild for each destination. Spend the time upfront to make each platform's version feel native.
Cost: Still free tier. I publish maybe twice a week, so 8-10 ops total.
Scenario 3: Keyword Input to AI Draft Delivery (15 modules)
This is the engine behind my Blog Post Generator tool. Someone submits a keyword. Make.com generates an outline via Claude, writes the full post, formats it, and emails it back.
The flow: Webhook receives keyword, call Claude API with system prompt (outline generator), parse Claude response, loop through each section calling Claude again (writer), aggregate all sections, format as HTML, send email with attachment.
This scenario taught me the most about reliability. The first version had zero error handling. If Claude returned malformed JSON, the whole thing broke silently. Now every Claude call is wrapped in a try-catch. If parsing fails, a notification emails me, and the user gets a "we're having trouble, try again in a moment" message.
The other huge lesson: Always chain AI calls instead of asking for everything at once. I originally asked Claude to "write a blog post" in one call. Token usage exploded, and response time was 45+ seconds. Now I ask for an outline first (fast), then write each section (parallelizable). Total time dropped to 18 seconds.
Cost: This one does cost. Maybe $0.08-0.15 per request (Claude API calls). I limit it to my community for now.
Scenario 4: Weekly KPI Digest (10 modules)
Every Monday morning at 9 AM, Make.com pulls data from Google Sheets, Stripe, and Airtable. It sends me a digest with key numbers and a brief AI-generated summary.
The flow: Schedule trigger Monday 9 AM, parallel fetch Google Sheets MRR plus Airtable customer count plus Stripe revenue, aggregate into JSON payload, call Claude to summarize (raw numbers to human-readable insights), format email, send.
Why this matters: I check this email first thing Monday. It's my weekly reality check. Is growth on track? Are there red flags? This scenario takes what would be 15 minutes of manual checking and does it while I sleep.
What I learned: Don't make digests too long. I started with 20 metrics. Now it's five key numbers plus a two-sentence AI summary. Claude helps here—I ask it to be brief and highlight only anomalies.
Cost: Free tier. Runs once a week. One Claude call (tiny). About 30 ops total per month.
Scenario 5: User Submits to Result Returned (9 modules)
This is the pattern behind all my live tools (Shadow Hound, Social Spark, etc.). User submits input via form. Make.com processes it. Result comes back as a file, email, or API response.
For Shadow Hound: Webhook receives resume plus job description, call Claude with prompt (compare resume to job, suggest improvements), parse response, format as PDF, return file or email.
This is my favorite pattern because it feels like actual software, but it's no-code. The user interaction is instant—they submit and get a result in 3-4 seconds. No waiting for admin. No queues.
What I learned: File handling is tricky. Generating a PDF from formatted text requires careful templating. I use a Google Docs template as the base, then Make.com uses the Google Docs API to replace placeholders with actual content, then exports as PDF. Took me three attempts to avoid corrupted PDFs.
Cost: Depends on volume. Each tool run uses one Claude call (~$0.001-0.003). I eat the cost on free tools, charge on premium ones.
Scenario 6: Error Monitor and Alert (3 modules)
All my critical scenarios send a heartbeat signal to a webhook. If a scenario hasn't reported success in 24 hours, I get an alert.
The flow: Schedule trigger (daily check), call Airtable (query last_success timestamp for each scenario), if any timestamp is older than 24 hours, send email alert to me.
This is paranoia automation. Nothing breaks in 24 hours often, but when it does, I want to know fast. I don't want a customer telling me a tool is broken. I want to know before they do.
What I learned: Heartbeat signals are cheap insurance. Each tool, after successful execution, writes a timestamp to Airtable. Cost is negligible. Value is huge. Caught a scenario that broke due to an API change I wasn't expecting.
Cost: Free tier. Barely registers.
How to Build Your Own Scenarios
If you're looking to start your own automation scenarios, here's my process:
- Start with the outcome. Not "I want to automate something." Instead: "I want my team to get a weekly digest without asking me." Then work backward.
- Map the manual process first. What steps do you do today? Write them down. That's your scenario skeleton.
- Find the trigger. Form submission? File upload? Time of day? That's where Make.com enters.
- Use parallel steps for speed. If two things are independent, do them simultaneously, not one after another.
- Wrap AI calls in error handling. Claude, OpenAI—they're powerful but occasionally fail. Plan for it.
- Add a monitor. Every scenario I care about has an error check. You'll be glad you did.
Start with something small. Not the 15-module beast. Maybe a five-module lead capture flow. Get it live. Iterate. Then build bigger.
These six scenarios handle probably 60% of my manual work. The time I save is spent building better tools, writing, and actually spending time with my family instead of doing busywork. That's the whole point.