AI assets proliferate in silos. Models live in SageMaker, agents live in private repos, prompts live in Notion pages, and datasets live in S3 buckets with no tagging. No team has the full picture.
Untracked assets create compliance failures. Auditors ask for a list of all systems that process personal data — and most enterprises discover they have far more than they thought.
Without version tracking, teams run deprecated models in production without knowing it. Model sunset dates pass unnoticed, and performance degrades silently.
Security gaps emerge from orphaned assets. A dataset with broad access permissions, attached to an agent whose owner left the company, is a breach waiting to happen.
Varman scans your cloud accounts (AWS, Azure, GCP), code repositories, SaaS tools, and network traffic to identify every AI asset — models, agents, prompts, datasets, and integrations. No manual cataloging required.
Each discovered asset is automatically enriched with ownership, version, framework, creation date, last-used timestamp, data sources accessed, cost estimate, and risk classification — sourced from existing tooling where possible.
Varman watches for drift between the registry and reality — new assets created, existing assets modified, deprecated assets still running. Daily reconciliation reports flag discrepancies before they become audit findings.
Discovers assets across AWS, Azure, GCP, on-prem servers, SaaS platforms, and private repositories.
Tracks every version of every model, prompt, and agent — with change history, author, and deployment timestamp.
Links every asset to an accountable owner, team, and cost center — enabling lifecycle management and offboarding workflows.
Auto-classifies every asset by data sensitivity, access scope, and regulatory exposure — prioritizing review effort.
Tracks assets from creation through deprecation with automated sunset alerts and decommission workflows.
Bidirectional sync with ServiceNow, Jira, and custom CMDBs — your asset registry stays in your tools of record.
Average AI assets discovered per enterprise
Asset coverage — cloud, on-prem, SaaS, repositories
Untracked models in production after full deployment
Time to complete full enterprise asset inventory
Complete your AI asset inventory in under 24 hours. No professional services required.