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A chatbot can sound helpful and still create a messy order.
When a customer changes a quantity or delivery detail, the real test is whether the system keeps the context, checks the information, and records the right request for the team.
In an 80-hour WhatsApp automation project, I built the conversation flow with structured AI output, input validation, order recording, logging, and error handling. The client rated the completed work 5.0.
That’s the part I care about most: turning a chat into a record people can actually work from, with errors visible when something goes wrong.
If your team takes orders through WhatsApp, what happens between the AI’s reply and the order record?
Order changes mid chat are where most WhatsApp bots fall apart. Did you confirm the changed details back to the customer before recording, or validate silently?
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An experimental exploration of bringing AI conversations to life through motion. ✨
Tool stack: Figma × Claude × After Effects
Really impressive
🟢 I built an AI agent that reads invoices so humans don't have to. Manual invoice entry is slow, boring, and full of typos. So I automated it end to end in n8n.⚫
Here's how it works:
🟢 Invoice comes in: A webhook receives the PDF through a POST request
⚫ Text extraction: The file is parsed and the text pulled out
🟢 Safety check: If the file is invalid, the system responds with an error right away
⚫ Smart chunking: Large invoices are split into smaller pieces so nothing gets cut off or lost
🟢 AI extraction: A loop sends each chunk to an Information Extractor powered by Anthropics Claude, which pulls out the fields the client needs (vendor, invoice number, dates, line items, totals)
⚫ Merge and validate: The results are combined into one record and checked before they are sent
🟢 Clean JSON response: The structured data goes straight back through the webhook The hardest part was'nt the AI. It was making it production-ready. 💡
Long, multi-page invoices break most simple AI workflows because they exceed the models limits. Chunking, looping, merging, and validating are what let this handle large invoices reliably. Where this fits in the real world:
⚫ Accounting and finance teams processing hundreds of vendor invoices
🟢 Businesses syncing invoice data into ERPs or spreadsheets
⚫ Freelancers and agencies automating their billing workflows What used to take minutes per invoice now takes seconds, with no copy-pasting. ⚡ I'm currently going deeper into n8n and AI automation.
If you are looking to automate document processing or any repetitive workflow, lets connect! 🤝
#n8n #Automation #AIAutomation #InvoiceProcessing #WorkflowAutomation #Claude #FullStackDeveloper #NoCode
High-converting mobile auto-detailing landing page designed for local service businesses. Built with quick-booking call-to-actions, clear pricing tiers, mobile-first responsiveness, and direct WhatsApp/Phone integrations to turn visitor traffic into instant bookings.
putting whatsapp and the phone number right in the booking flow makes a lot of sense for a mobile detailer, they're usually out on a job. how are you getting in front of local service businesses at apex, cold outreach or referrals