In partnership with
Day 59 with OpenClaw & LinkScopic Data Stacks
Today we pushed the system a little further.
We added Claude CoWorker skill sets into OpenClaw to introduce a deeper agentic analysis layer on top of our LinkScopic data stacks. The goal was simple: see what happens when an AI agent is given structured retail datasets and asked to perform true product intelligence analysis.
The results were honestly shocking.
For the test run we used Joe’s New Balance Outlet as the source store. The system ingested the product dataset and immediately began cross-referencing pricing against other retailers.
Within minutes it produced:
• Full spreadsheets of matched products
• Pricing comparisons across retailers
• ROI calculations
• Charts and structured summaries
• A full written analysis of the findings
No manual searching. No manual spreadsheets. No manual comparisons.
Just structured data + an agent capable of reasoning through it.
The Dataset
The scan covered 23,333 New Balance products from Joe’s.
From that dataset the agent found:
2,212 products with Amazon pricing matches
767 products with Walmart pricing matches
1,248 products showing 18%+ gross ROI
After applying estimated marketplace costs:
• ~15% referral fee • ~$5.50 fulfillment cost
The agent narrowed everything down to 100 unique products with 10%+ net ROI.
That’s where things started getting interesting.
Top Findings from the Agent
Some of the highest ROI opportunities identified:
NB Kids' 1000 Beige Buy: $39.99 Sell (Walmart): $99.95 ~99% Net ROI
NB Women's 997H Pink/Beige Buy: $69.99 Sell (Amazon): $160 ~87% Net ROI
NB Women's BurnX5 Limited Edition Buy: $107.99 Sell (Walmart): $242.91 ~86% Net ROI
NB Unisex 442 PRO FG V2 Buy: $49.99 Sell (Walmart): $91.99 ~84% Net ROI
And those were just the top four.
The full output included dozens of viable opportunities, all ranked, analyzed, and presented in clean structured outputs.
What Makes This Different
The key here isn’t just that the agent found profitable products.
The key is how quickly it happened.
Normally this kind of analysis requires:
Manual store browsing
Product matching
Price comparisons
Spreadsheet building
ROI calculations
Market checks
That process can take hours or days.
This agent did it in minutes.
Because it wasn’t working from scraped pages or manual inputs. It was working from clean, structured LinkScopic datasets running through OpenClaw pipelines.
That’s the real unlock.
Infrastructure Changes Everything
When you combine:
• Massive product datasets
• Cross-retail price intelligence
• Agentic reasoning
• Structured data pipelines
You stop asking “What products should I check?”
Instead the system starts telling you:
“Here are the opportunities.”
Where This Is Going
This was just a test run.
But it showed something important.
Agents paired with structured data stacks don’t just answer questions.
They discover opportunities.
And as we continue layering more datasets, demand signals, and retailers into the system, the analysis will only get deeper.
Today it was Joe’s New Balance.
Tomorrow it could be any retailer in the dataset.
And that’s where product intelligence starts to become something entirely different.
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