Unlock significant savings and drive sustainable procurement with AI-powered spend analytics.
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Sievo Spend offers paid plans. Visit their website for current pricing details.
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Sievo specializes in integrating data from complex landscapes, automatically extracting information from all your ERPs, data lakes, and procurement systems. It then cleanses, translates, and de-duplicates this data to create a unified, reliable single source of truth, even for organizations with diverse data sources.
Sievo IQ is a conversational analytics feature that allows users to ask specific questions about their spend data. It leverages AI to provide instant, accurate answers and uncover savings opportunities and high-impact optimizations by analyzing internal, external, and Sievo's cross-customer data.
Sievo Spend offers full visibility into sustainability performance, enabling actions to drive compliance with frameworks like CBAM, CSRD, and GRI. It helps in reducing Scope 3 emissions, mitigating risks, and building a sustainable, resilient, and diverse supply chain by tracking and analyzing supplier sustainability targets.
Yes, Sievo provides proactive supplier normalization by combining AI, a cross-customer Supplier Database, and Dun & Bradstreet data. This ensures accurate normalization results and delivers validated updates, such as M&As, without manual effort, allowing for better supplier relationship management.
Sievo's Community Data refers to anonymized cross-customer insights, recommendations, and benchmarks. This data enriches your internal spend data, providing a broader context for decision-making and helping to identify additional working capital opportunities, particularly within Payment Term Analytics.
Sievo guarantees the industry's highest spend classification quality, regardless of taxonomy customizations, with over 98% coverage and 94% accuracy. Its AI-powered classification incorporates validated learnings from its extensive cross-customer Community Data, allowing for continuous improvement and easy re-classification if a mistake is spotted.
Source: sievo.com