Today I helped fix a small but blocking issue in an n8n workflow that looked simple on the surface but kept failing at the API step. The flow was straightforward Webhook to HTTP request to Google Sheets and then an email notification The webhook was receiving data correctly but the workflow kept stopping at the HTTP request node. After digging in I found the issue was a mix of API authentication and how the JSON response was being handled. I debugged the HTTP request node checked headers tokens and payload structure then fixed the authentication logic. I also cleaned up the JSON parsing so the data mapped correctly into Google Sheets without breaking the execution. After that I ran multiple end to end tests to make sure the workflow completed successfully every time and triggered the email notification as expected. This is a good reminder that most automation failures are not about complexity. They usually come down to small details in API calls and data structure. Once those are right n8n workflows become very stable and predictable.

Today I worked on a small proof of concept for a B2B lead generation setup called NexaGrowth. The goal was to see how AI could quickly qualify incoming leads without adding complexity. The workflow starts with new leads coming in through a Google Form and webhook trigger. Once a submission comes in, the data is sent to OpenAI where the lead is analyzed to classify the industry, estimate company size based on the description, and assign a lead score from 1 to 10. That output is then structured cleanly and pushed into Airtable so the data is easy to review and filter later. If the lead score comes back as 8 or higher, the automation instantly sends a Slack notification so the team can act fast on high intent leads. Nothing fancy, just clean logic, clear prompts, proper data mapping, and basic error handling to keep the flow stable. This kind of setup is a great example of how AI can support lead qualification without replacing existing systems or overengineering the process. Simple automations like this often deliver the quickest wins.

One thing I’ve learned working on n8n projects across different teams is that phone calls and text messages are usually where automation breaks first. When a lead calls or sends a text about a home sale, timing and accuracy matter more than anything. So the first thing I focus on is capture. Every call or SMS needs to land in one place with context attached. Who contacted us. Where they came from. What they asked. From there the logic matters. Leads are routed based on rules that make sense for the business not just pushed blindly into a CRM. Some go to sales. Some trigger follow ups. Some need a callback task created immediately. CRM automation ties it all together. Updates happen automatically. Notes are added. Status changes are logged. No manual copying. No missed steps. Reliability is the part most people ignore. Webhooks fail. Phone systems timeout. APIs return bad responses. In n8n I always build retries logging and safeguards so one failed step does not break the entire flow or create duplicates. When done right the team does not think about the automation at all. Calls come in. Messages are handled. Leads move forward quietly. That is usually the sign the system is doing its job.

A lot of automations look simple on the surface. A form is submitted. Data shows up somewhere else. Done. But behind the scenes, there’s a lot more happening. I spend most of my time building workflows in Make and n8n that sit between frontend tools and backend systems. Forms from Webflow, WordPress, React apps, or custom setups flow into CRMs, databases, and internal tools through APIs and webhooks. The real work is in the logic. Validating incoming data. Handling failed webhooks. Retrying safely without creating duplicates. Making sure one bad request doesn’t break the whole flow. More recently, I’ve been embedding AI into these systems. Using OpenAI and LLM based agents to process inputs, make decisions, and push clean structured data into the right systems automatically. A good automation isn’t just about connecting apps. It’s about reliability, performance, and clarity. If something breaks, it should be obvious why. If someone else takes over, they should understand how it works. That’s the part most people never see, but it’s what makes the difference long term.

I recently built a workflow where publishing a blog post automatically handled social distribution without extra steps. Once a post is published on WordPress, the workflow picks it up immediately. The content is sent to ChatGPT, where it’s rewritten into platform specific formats for Twitter, LinkedIn, and Facebook. Each platform gets its own tone, structure, and hashtags without manual editing. The tricky part was control. After generation, the content is parsed using regex and routed correctly so each platform only receives what it needs. Twitter gets short text, LinkedIn gets a longer post with an image pulled directly from the article, and Facebook combines the post text with the original link. Everything runs through one logic path with filters instead of separate workflows. No duplicates. No reposting errors. No copy paste. Publishing the blog became the only action needed. Distribution happens quietly in the background. This is the kind of automation that doesn’t look fancy, but removes a lot of friction once it’s live.