Automated claims extraction across formats and languages, reducing manual QA by over 70%, accelerating approvals, and cutting compliance risk at scale.
In fast-moving industries like consumer goods, healthcare, and retail, verifying product claims across thousands of SKUs, languages, and formats is critical for compliance and brand integrity. Yet most teams rely on manual review processes that are slow, error-prone, and hard to scale.
Blackstraw delivered an AI-first Product Claims Extraction and Generation Engine that automates the identification, extraction, and validation of claims from images, marketing materials, and structured data—enabling faster approvals, lower compliance risks, and reduced operational costs.
Multi-Format Data Ingestion: Supports images, CSV files, and web-scraped sources with advanced OCR for multilingual, low-quality scans.
Advanced Claims Extraction: Combines Graph Neural Networks and custom Large Language Models to understand label context and surface precise claims.
Refined Output with Voting Model: Ensures high-confidence results by blending outputs from multiple AI layers, reducing false positives.
Rule-Based Cleanup: Customizable logic to eliminate irrelevant or out-of-scope claims tailored to industry and regulatory needs.
Seamless Integration: Robust API to push refined claims data into CMS, regulatory reporting, or marketing approval systems.
Flexible, Modular Deployment: Adopt as a full-stack platform or in phases, scaling with your needs and maturity.
Manual Effort Reduction: 70%+ reduction in manual QA, lowering review time per SKU.
Faster Time-to-Market: Accelerated packaging and marketing claim approvals.
Stronger Compliance: Maintains consistent, auditable claim records across languages and geographies.
Scalable for Growth: Single platform adaptable across brands, markets, and high-SKU portfolios.
Cost Efficiency: Frees up legal, labeling, and product teams, reducing operational overhead.
Enjoying this article? Share it with the World
