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Written by
John
Published on
June 5, 2025
Scalable Deduplication for Global Business Intelligence
- Challenge: Product name variations created unreliable data and inflated compute costs.
- Solution: A scalable, low-cost AI system for deduplication with near-LLM accuracy.
- Impact: 100x cost reduction with high-quality data and better insights.
Context
We partnered with a global pricing intelligence provider to solve a core data challenge—consolidating product listings with slight naming differences across markets to deliver accurate business insights at scale.
The Challenge
This client collects pricing data across industries and geographies, enabling their customers to monitor and compare competitor prices worldwide. However, they faced two major issues:
- Duplicate Product Variants: Identical products often appeared under different names or descriptions across regions, leading to noisy, unreliable data.
- Unscalable Matching: Commercial AI models like ChatGPT could group these products accurately—but were cost-prohibitive due to the billions of possible product pairings.
Even small mismatches caused cascading errors in metrics like inflation tracking, regional pricing trends, and market share analysis.
Our Custom Solution
We engineered a highly optimized, task-specific AI system that:
- Accurately detects duplicate and near-duplicate product listings across vast datasets.
- Uses customized embeddings to mimic the accuracy of LLMs like ChatGPT—at a fraction of the cost.
- Operates efficiently across billions of item combinations, enabling true market-scale insights.
The Impact
- 100X Cost Efficiency: Our solution delivered nearly the same accuracy as commercial LLMs while being 100 times more affordable.
- Actionable Data: Enabled more reliable, consolidated insights for clients analyzing price trends across regions.
- Competitive Advantage: The client now offers cleaner, high-fidelity datasets for downstream analysis and benchmarking.
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