Make Your Data AI-Ready in Days
Discover, classify, and govern your unstructured data across every source — without manual cleanup or risky migrations.
What's blocking your <strong>AI initiative</strong>
The data problems that stall enterprise AI before it ships.
Unclassified data
Compliance exposure
Slow time-to-value
Beyond point-in-time compliance
| Capability | Traditional Approach | Aparavi |
|---|---|---|
| Data discovery | Manual sampling | Automated, 50+ sources |
| Classification | Single-snapshot scan | Continuous, 1,600+ types |
| Risk visibility | Quarterly reports | Real-time dashboards |
| AI readiness | Months of cleanup | Days to first insight |
We went from quarterly compliance audits to continuous risk visibility — and our AI initiative finally has a data foundation we trust.
AI Readiness <strong>Engagement</strong>
30 days. Fixed scope. Concrete deliverables your board will read.
Fragen
How long does the engagement take?
What data sources are supported?
Where does my data actually go?
Ready to make your data <strong>AI-ready</strong>?
30 days. Fixed scope. Real answers about your data estate.
Request engagementWhat you get out of the box
Built for the unstructured-data reality of modern enterprises.
Discovery
Scan 50+ data sources without copying or moving files.
Classification
1,600+ file types automatically identified and tagged.
Governance
Continuous policy enforcement with full audit trail.
How the engagement runs
Four steps from kickoff to delivered roadmap.
Alignment
Define scope, sources, and success criteria with stakeholders.
Discovery
Scan and classify every connected source - in place, no copies.
Analysis
Quantify risk, ROT data, and AI readiness across departments.
Delivery
Executive report, roadmap, and findings walkthrough.
Built for these segments
Each segment has different drivers — same Aparavi platform handles them all.
Health Systems
Patient data, HIPAA, audit trails
Insurance Carriers
Claims, PII, retention policy
Energy Utilities
SCADA, NERC CIP, IP
Regulation coverage
| Regulation | What it requires | Aparavi coverage |
|---|---|---|
| GDPR Art. 30 | Records of processing | Auto-generated inventory |
| HIPAA | PHI access controls | Continuous classification |
| PCI-DSS | Cardholder data scope | Real-time scope reduction |
| SOC 2 | Continuous monitoring | Automated audit trail |
Without governance vs with Aparavi
Without continuous governance
- Sensitive data leaks into AI training
- Permissions drift; access not in sync
- Quarterly audits, lagging indicators
- Months of manual cleanup before AI
With Aparavi continuous governance
- Sensitive content flagged before training
- Permissions synced with classification
- Real-time risk dashboards
- Days to first AI-ready dataset
Book a discovery call
Stop fighting your data, start governing it
Spreadsheet of data sources, manually maintained
Automated scan of 50+ source types in place
Rule-based, brittle, requires constant tuning
1,600+ file types, ML-based
Where does your data score?
Five dimensions, weighted, scored 0-100.
Built for automation
# Classify a folder against the standard PII / PHI / PCI rules
aparavi classify --source /mnt/share \
--rules pii,phi,pci \
--output report.json
# Inspect findings
jq '.summary' report.jsonGet your AI Readiness score
Free assessment of where your unstructured data stands across five dimensions: Security, Quality, Accessibility, Classification, and Governance.
Start free scan Talk to an expertFive Weighted Dimensions
A quantified, evidence-based score (0-100) that measures your ability to deploy AI safely and effectively.
AI Readiness Score = (S × 0.35) + (Q × 0.25) + (A × 0.20) + (C × 0.12) + (G × 0.08) -
Security
35%PII / PHI detection, legal-privilege content, IP sensitivity, external sharing exposure, permission-inheritance risk.
Why it matters: AI multiplies access. Security readiness determines safe deployment.
-
Data Quality
25%Duplicate ratio, obsolete and trivial content, extractable formats, error patterns.
Why it matters: Low-quality data leads to unreliable AI outputs and unnecessary compute cost.
-
Accessibility
20%AI-compatible formats, metadata completeness, OCR / transcription needs, structural consistency.
Why it matters: AI must parse before it can reason.
-
Classification & Dataset Readiness
12%Department coverage, classification confidence, compliance tagging, dataset-segmentation potential.
Why it matters: Enterprise AI runs on governed datasets — not raw file systems.
-
Governance
8%Ownership clarity, retention enforcement, policy alignment, operational repeatability.
Why it matters: AI must be auditable and defensible.
Significantly less manual effort. Same team.
Before Aparavi
After Aparavi
Operational Impact
Finally, risk numbers the board understands
What the Board Wants
Executive Dashboard Includes
- One-page risk summary
- Trend analysis
- Peer comparison
- Remediation progress
- Investment recommendations
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