Deploy Enterprise AI Faster, Smarter, and Without Increasing Risk

The average enterprise manages petabytes of unstructured content spread across dozens of systems: SharePoint libraries, shared drives, cloud storage buckets, email archives, and legacy file servers. Most of this content was never classified, never inventoried, and never reviewed for sensitivity. It accumulates year over year, and nobody knows exactly what's in it.

This creates three compounding problems. First, sensitive information (personally identifiable records, protected health information, intellectual property, financial statements, credentials) is scattered across the environment without visibility. Second, permission sprawl means that content is often accessible to far more users than intended, creating compliance exposure that grows with every shared link and inherited permission. Third, a significant portion of stored content is redundant, obsolete, or trivial, consuming storage budget and polluting any AI model trained on it.

Traditional approaches to solving these problems (manual audits, sample-based reviews, spreadsheet tracking) don't scale. They take months, miss the majority of content, and produce findings that are outdated before the project finishes. Aparavi replaces that approach with automated, continuous, in-place analysis that covers every file across every connected source.