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Built an AI system that reads supplier invoices and posts them straight into a dealer management system. No one retypes anything anymore.
The problem: a group of car dealerships was getting hundreds of supplier invoices by email and WhatsApp photos, and staff were keying every line into their DMS by hand.
What I built:
• Email and WhatsApp intake, auto-matched to the right dealership
• AI and vision extraction of vendor, tax details, and line items grouped by vehicle
• Verification and duplicate detection before anything gets posted
• A review dashboard for the few cases that need a human
• Browser automation that posts approved invoices into the DMS
• WhatsApp auto-replies that chase suppliers for missing details
Result: 280+ invoices processed so far, with zero exceptions.
Stack: Python, LLM and vision models, React, Selenium
If your team is still copying data from documents into another system, that's usually very automatable. Happy to take a look at yours.
#AI #Automation #AIAgents
280+ invoices with zero exceptions is the number that sells this. The WhatsApp intake auto-matched to the right dealership is smart, and keeping a review dashboard for the edge cases shows you trust the system without being naive about it.
The duplicate detection and review dashboard are the right instincts. The failure I'd guard hardest is the confidently-wrong field: a total or tax that parses cleanly but reads the wrong number, since it never looks low-confidence enough to reach your dashboard. What saved me...
I'd test a DMS timeout immediately after the invoice saves, since Selenium can lose the confirmation even when the entry exists. Before retrying, does your flow check the dealership, supplier and invoice number in the DMS so it can recover without posting twice?
The WhatsApp auto-replies that chase suppliers for missing details are the piece I like most, since that closes the loop instead of just flagging a gap. How do you decide when a missing field gets an automatic chase versus going to the review dashboard for a person?
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🚀 Turning Manual E-commerce Order Processing into an Automated Workflow with n8n
Manual order management may seem simple, but copying customer details, checking information, preventing duplicate orders, and sending confirmation emails can quickly become repetitive and error-prone.
I recently built an end-to-end order processing automation using n8n to solve these challenges for a handmade products store.
Here's what I implemented:
🔹 Automated Order Capture: Receive new orders through a webhook.
🔹 Data Validation & Cleaning: Validate required fields, email formats, quantities, and prices before processing.
🔹 Duplicate Prevention: Detect existing Order IDs to prevent duplicate records.
🔹 Google Sheets Integration: Automatically store valid orders and calculate order totals.
🔹 Email Notifications: Send order updates to the store owner and confirmation emails to customers.
🔹 Error Monitoring: Capture failed executions, log error details in a dedicated Google Sheets tab, and send email alerts.
The goal was simple: reduce repetitive administrative work, improve data accuracy, and make order operations more reliable.
This is what I enjoy about building with n8n — turning complex, repetitive business processes into practical automation systems.
Have a repetitive workflow in your business that could be automated? Let's connect!
#n8n #WorkflowAutomation #EcommerceAutomation #BusinessAutomation #NoCode #Automation
Using AI Automation workflow in business , not only save the time ,also increase work Efficiency
This year’s YC Fall batch is for us creatives. 🎯
As a product designer, I’ve never felt like our field was changing as rapidly as it is right now. Tools emerge, you adapt, they disappear. Processes that took hours can now be largely automated, and overall, I feel like a big part of the fun gets lost because of a communication issue: I struggle to tell the tools exactly what I want in enough detail.
I recently stumbled across a few interesting and promising startups that might bring some of that joy back and take the pain out of parts of our work.
Proto is building an IDE specifically for product designers, bringing design and AI workflows closer together. 👩🏻💻
Simulithic uses AI agents to test your app across devices, browsers, and account types, automating test creation and maintenance. 🤳🏼
Composal uses swarms of AI agents to explore product flows and edge cases, finding what breaks before release. 🕵🏻♂️
Native helps marketers decide what to create by ranking ideas based on brand context, performance, trends, and creative feedback. 🦖
The edge case testing sounds promising.
An AI customer support chatbot for a fictional Nigerian food delivery business, Lagos Fresh Kitchen. It runs on n8n Cloud and uses Claude Haiku 5.5 as the AI model. The workflow is three nodes: a chat trigger receives the customer’s message, an AI Agent processes it, and the Anthropic Chat Model writes the answer.
How it behaves
The AI Agent has a system message that sets the rules. It must answer only from the business information, never invent prices, areas, hours or policies, and use a fixed fallback line when the answer isn’t there: “I don’t have enough information to answer that accurately. Please contact our support team.”
The business information it knows
Open Monday to Saturday, 9 AM to 8 PM, closed Sundays
Delivers to Lagos Mainland (Ikeja, Yaba, Surulere, Maryland, Ojota, Gbagada) and Island areas (Victoria Island, Ikoyi, Lekki), not outside Lagos
Delivery takes 30 to 60 minutes, with fees from ₦1,000
Menu of jollof rice, fried rice, rice and stew, pounded yam with egusi, grilled chicken, suya and soft drinks
Payments by card, transfer and cash on delivery
Cancellation allowed before cooking starts
No Sunday orders or Sunday delivery, and no event catering
Refunds, complaints and order status go to support
good work