A global CPG manufacturer with a large digital retail presence faced challenges in keeping product information accurate and consistent across millions of SKUs, including pricing, promotions, and assortment data. Fragmented data sources and manual collection slowed down decision-making and restricted visibility across channels. Blackstraw helped the company shift toward nearly real-time product intelligence. Reducing manual effort by up to 45% and speeding up the insights delivery at scale without raising ongoing operational costs.
Retail and CPG enterprises often deal with scattered product data. This makes it hard for them to react quickly to changes in pricing, promotions, and product selection. Manually collecting data adds extra hurdles, raising costs, slowing growth, and restricting how competitively teams can operate. The client was in this exact situation.
Blackstraw worked with them to eliminate these product data bottlenecks by providing a scalable, AI-powered product intelligence platform that helps support quicker, more confident decision-making in the modern digital commerce landscape.
Comprehensive Data Acquisition: Scrapes product, pricing, and promotion data from 1,300+ retail websites, covering 7.2M+ products with frequent, automated updates.
Accurate Product Recognition: Achieves 90%+ accuracy in identifying and classifying products using advanced AI and computer vision models.
Automated Receipt & Promotion Extraction: Delivers 95% automation and 92–95% accuracy in extracting key product and pricing details from receipts and promotional flyers.
Advanced Claims Intelligence: Extracts and normalizes 100+ product attributes and claims using NLP and AI, enabling detailed category and brand level analytics.
Scalable, Production-Grade Architecture: Operates at enterprise scale with reusable pipelines, multi-language support, and high-throughput processing—without proportional increases in cost.
Continuous Product Visibility: Always-on monitoring of millions of SKUs across digital retail channels, enabling faster responses to assortment, pricing, and promotion changes.
Significant Efficiency Gains: Reduced manual product intelligence and coding effort by 35–45%, allowing teams to focus on higher-value analysis.
High Accuracy at Scale: Delivered 90%+ product recognition accuracy and 92–95% extraction accuracy across unstructured retail data sources.
Near–Real-Time Intelligence: Automated refresh cycles support near–real-time insights, particularly for competitive analysis and execution decisions.
Cost-Efficient Automation: Achieved 95% automation across key workflows without increasing operational overhead, enabling scalable expansion across markets.
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