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SmartCrop – AI Smart Farming Assistant
SmartCrop is an AI-powered platform designed to help farmers make better agricultural decisions. It provides crop recommendations, pest detection using computer vision, weather-based insights, and an AI farmer chatbot.
Farmers can interact with the system through voice or text in multiple languages, making it accessible for rural communities. By analyzing agricultural data such as soil conditions, crop datasets, and weather patterns, SmartCrop delivers intelligent recommendations to improve crop yield and reduce farming risks.
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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
End-to-end retail analytics pipeline that turns raw, messy CSV exports into business decisions, built in Python and SQL and runnable with one command.
What it does:
Cleans and validates 6 relational tables, fixes 585 data-quality issues and logs every action
Loads a SQLite warehouse with keys, constraints and window-function SQL
Customer analytics: RFM segmentation, cohort retention, CAC / LTV by channel, product Pareto, market-basket analysis
Churn prediction model validated on future data (ROC-AUC 0.71, no data leakage)
6-month revenue forecast, best of 3 models by backtest (4.2% MAPE)
Auto-generated Excel report (live formulas, native charts) and an interactive Streamlit dashboard
18 automated tests
The fix for an agent that keeps getting your design system wrong isn't a better prompt.
It's putting the rules where the agent reads them.
In the repo. In skills. In checks it can run itself.
Writing a course about exactly this.
What sells it for me is the refusal telling the agent exactly where the token should go. A bare "no hex" error usually just sends it looping. Does guard.mjs run only as an agent hook, or in pre-commit too so it catches humans as well?