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.

I recently worked on an automation where the input was simple but the logic was not. A Google Sheet from Outscraper containing business names and addresses. Sometimes a website. Sometimes a phone number. Emails were often wrong or missing. The goal was to enrich every single row without skipping anything. The workflow runs row by row in n8n. Even if an email already exists, the automation still rechecks the business. No assumptions. First step is controlled HTTP scraping. If a website exists, the automation checks key pages like homepage contact about and team. Text is capped and cleaned immediately to avoid token overload. Next comes AI reasoning. Instead of free text responses, the model is forced to return strict JSON only. Owner name email source confidence and notes. If the owner cannot be found, it returns blanks with a reason. No hallucinations. Email extraction happens from both scraped pages and AI inference but nothing is trusted yet. Every email then goes through Reoon verification with daily rate limits enforced. Batch control makes sure we never exceed the quota. Failed verifications are logged not retried endlessly. The sheet is updated in place. Correct columns only. No overwriting without intent. Idempotency checks prevent duplicates and infinite loops. Failures do not crash the workflow. HTTP errors timeouts missing websites all fall into a handled path with logs. End result is a clean verified CSV that can actually be used. Outscraper sheet in. Owner name and best email out. Verified. Logged. Reliable. This type of automation is where AI is useful only when the guardrails are tight.

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.

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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.