Turn agricultural market data into actionable insights. I build custom data and analytics solutio...Turn agricultural market data into actionable insights. I build custom data and analytics solutio...
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Turn agricultural market data into actionable insights.
I build custom data and analytics solutions for agriculture businesses, including market price comparison, competitor monitoring, supplier research, and product intelligence.
I can collect publicly available data from relevant sources and turn it into a clean dashboard where you can compare products, suppliers, prices, countries, and market trends in one place.
The system can be built for different agricultural categories, from machinery and equipment to seeds, crops, fertilizers, and other products.
What I can build:
Agricultural web scraping & data collection
Product and price comparison
Competitor and supplier monitoring
Market trend analysis
Custom analytics dashboards
Data filtering, categorization & normalization
AI-powered insights and summaries
API integration and automated data workflows
Every project is customized around the data you need and the way your business works.
Have a specific agricultural data problem? Feel free to contact me and let's discuss what can be built.
Everyone thinks a data dashboard should look complicated.
I think that is exactly why most of them fail.
I just finished Helios, a tool that tracks solar energy assets in real time. Dozens of sites, live output, verification records, a wall of numbers. The kind of screen people drown in.
Most designers would make it feel technical. More lines, more glow, more charts. I did the opposite. Every number had to answer one question in half a second, is this good or is this a problem. If it could not, it lost its place.
Because design is not decoration, it is closer to math. It is deciding what to remove until only the truth is left.
Complexity was never the enemy. Confusion is.
Get that right, and people trust you before you have said a word.
Lost 2 days building a complex n8n workflow. Here's what I learned.✌️
The project: A 12-node AI Receptionist automation for a luxury resort in Bangladesh. Multi-channel (WhatsApp, Telegram, Voice), 9 Google Sheets, Gemini API, payment automation, customer CRM.
What went wrong: Added a new voice integration node. Accidentally deleted the entire workflow.
Why it happened:
No JSON exports after milestones
No version control (Git)
Testing changes LIVE on production workflow
No documentation
Building locally with zero redundancy
The fix (now implemented):
✅ JSON backups after every 2-3 nodes
✅ GitHub versioning
✅ Clone workflows for testing
✅ Modular design
✅ Complete documentation
For automation professionals: You're not just builders—you're engineers.
Treat workflows like software:
Documentation
Version control
Backup strategy
Modular testing
Client delays = lost revenue + credibility damage.
Make the systems bulletproof.
How do you prevent data loss in your automation builds? 👇
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