Your most impactful decisions,
down to a science.
In today’s data-driven world, important decisions are made without knowing whether the underlying data is fit for purpose. So, we asked, why rely on data that’s probably right?
Who we are
We help you make better, faster, and more insightful decisions
We are a Silicon Valley technology company that enables organizations to make more informed and confident decisions in their mission-critical workflows. We began in the financial industry, but today, we have solutions in other verticals, too.
Monitor data quality, analyze business impact, and make your data fit-for-purpose
Cuneiform® enables users to monitor data quality metrics, identify high-impact exceptions, and perform faster root-cause analysis across internal and external datasets in real-time.
“Using the Cuneiform SaaS Platform for data quality, Broadridge is reducing costs associated with onboarding, driving efficiency, and improving time-to-value, while confidently expediting its customer go-live dates.”
Danny Green, Head of Post-Trade Processing, Broadridge International
See what our platform can do for you
Cuneiform® enables users to monitor data quality metrics, identify high-impact exceptions, and perform faster root-cause analysis across internal and external datasets in real-time.
Cuneiform® for Valuation Risk is an intuitive platform used by financial institutions to improve the fair-value estimation of assets and ease the P&L creation process by providing scenario and trend analysis, price backtesting, and contextual views of instrument prices using various datasets.
Cuneiform® for CRM (Salesforce) is a business data reliability application that enables CRM Admins, data stewards, and implementation teams to rapidly assess and quantify data challenges, prioritize business risks, and effectively manage improvement efforts and support desired business objectives.
Cuneiform® for Data Cloud is the first data management application on and for Salesforce Data Cloud that enables organizations to accelerate and derisk implementations, continually assess data quality against fit-for-purpose metrics, and reduce operational costs incurred by unnecessary reprocessing.
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