How a Small Experiment Began
Last month, over 200 order confirmation documents were piling up on the Windows device. Manually verifying formats and checking for errors across different trading partners took forever. "Could a bot validate these automatically?" This small question sparked an experiment.
First, we organized the core fields of order data in Google Sheets. Trading partner, product name, quantity, shipping date, special requests, and more. Then we gave Claude Code a simple instruction. "Learn each trading partner's order format and flag any items that deviate from the pattern."
When AI Exceeded Expectations
On the first run, the bot caught obvious typos and blank fields. Ordinary results. But from week two onward, interesting things started happening.
The bot began detecting patterns on its own, like "Partner A always schedules shipments on Tuesday, but this order came in for Thursday." The developer didn't input individual rules. The AI was learning partner preferences simply by observing the data.
Another example. Partner B's orders frequently had blank "Product Code" fields, but the bot didn't classify it as a simple error. Instead, it recognized it as "This partner places orders by product name." Same situation, different interpretation based on context.
Hidden Problems Surface
Unexpected data issues emerged during the automated validation process.
First, order confirmations from trading partners had varying character encodings. Same information, but some files used UTF-8 while others used different encodings. The bot automatically detected this, and we learned about the problem too.
Second, date formats were mixed. "2024-01-15," "01/15/2024," "15-01-2024", all appearing together. To human eyes they were the same day, but to automation they were errors.
Third, most surprisingly, the bot detected "repeat order" patterns. Some orders recurred every three months. When the bot flagged this, it even asked, "Did you realize this month's order is missing?" We had indeed overlooked a few orders.
Small Automation, Big Impact
Now, every Monday morning, the bot generates a validation report automatically. Problematic orders are sorted by priority, each with recommendations. We respond within five minutes of reviewing.
But the bigger impact is how this accumulated work became a catalyst for improving how our team works with trading partners. The bot's reports clearly show each partner's characteristics, letting us standardize our approach by partner.
"The real value of automation isn't processing work faster. It's discovering hidden patterns along the way."
Another small experiment is in the works now. This time, the bot will track trading partner response times and flag orders that might miss their expected shipping dates before it's too late. We're curious what other unexpected discoveries await.