The rebuild makes readable what earlier technology could not capture – up to 75% of Sayari’s archive – and is expected to cut data infrastructure costs by more than 50%
WASHINGTON, Sept. 2, 2026 /PRNewswire/ — Sayari, the trusted AI platform for economic security and commercial risk, today announced it has selected Snowflake (NYSE: SNOW), the AI Data Cloud company, to rebuild the Commercial World Model, Sayari’s map of the world’s companies, the people behind them, and the trade between them. The rebuild turns more than a decade of primary-source data, 12 billion records from 715 sources across 250 jurisdictions, into a single AI-ready foundation for every Sayari product.
Sayari’s archive includes roughly 1 billion original documents including corporate filings, government gazettes, and shipping records in their original published form. For more than a decade, Sayari has built extraction pipelines that parse companies, people, and relationships from those documents with the precision its customers require. But deterministic extraction has limits. Sayari estimates that up to 75% of the useful information in its documents, the context and patterns surrounding those records, could not be captured that way. Advances in AI now put that information within reach, and rebuilding on Snowflake lets Sayari apply those techniques across the entire archive at once, not document by document.
Customers already rely on Sayari to identify who owns and controls companies and to detect risk across supply chains and business relationships. The rebuilt platform lets them go deeper, faster, and at greater scale: tracing ownership further, detecting risk earlier, and surfacing entirely new classes of risk intelligence from patterns no single record contains, with every finding traceable to the primary sources behind it.
The Commercial World Model brings this data together in one place, continuously updated, connected, and analyzed. Sayari collects it directly from official sources, including corporate registries, customs bureaus, and regulatory agencies, in more than 20 languages and scripts. Much of this is deep web data that has never been indexed including records invisible to search engines and to the general-purpose AI tools trained on the open web, and in many cases no longer available from their original publishers. Sayari’s archive is often the only place they exist in accessible form.
Sayari’s engineering team used Snowflake CoCo, Snowflake’s AI coding assistant, to accelerate the migration. The move is expected to reduce Sayari’s data infrastructure costs by more than 50%, according to the company’s projections.
The rebuild makes readable what earlier technology could not capture – up to 75% of Sayari’s archive – and is expected to cut data infrastructure costs by more than 50%
WASHINGTON, Sept. 2, 2026 /PRNewswire/ — Sayari, the trusted AI platform for economic security and commercial risk, today announced it has selected Snowflake (NYSE: SNOW), the AI Data Cloud company, to rebuild the Commercial World Model, Sayari’s map of the world’s companies, the people behind them, and the trade between them. The rebuild turns more than a decade of primary-source data, 12 billion records from 715 sources across 250 jurisdictions, into a single AI-ready foundation for every Sayari product.
Sayari’s archive includes roughly 1 billion original documents including corporate filings, government gazettes, and shipping records in their original published form. For more than a decade, Sayari has built extraction pipelines that parse companies, people, and relationships from those documents with the precision its customers require. But deterministic extraction has limits. Sayari estimates that up to 75% of the useful information in its documents, the context and patterns surrounding those records, could not be captured that way. Advances in AI now put that information within reach, and rebuilding on Snowflake lets Sayari apply those techniques across the entire archive at once, not document by document.
Customers already rely on Sayari to identify who owns and controls companies and to detect risk across supply chains and business relationships. The rebuilt platform lets them go deeper, faster, and at greater scale: tracing ownership further, detecting risk earlier, and surfacing entirely new classes of risk intelligence from patterns no single record contains, with every finding traceable to the primary sources behind it.
The Commercial World Model brings this data together in one place, continuously updated, connected, and analyzed. Sayari collects it directly from official sources, including corporate registries, customs bureaus, and regulatory agencies, in more than 20 languages and scripts. Much of this is deep web data that has never been indexed including records invisible to search engines and to the general-purpose AI tools trained on the open web, and in many cases no longer available from their original publishers. Sayari’s archive is often the only place they exist in accessible form.
Sayari’s engineering team used Snowflake CoCo, Snowflake’s AI coding assistant, to accelerate the migration. The move is expected to reduce Sayari’s data infrastructure costs by more than 50%, according to the company’s projections.
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