How it will work
The app is designed to run as a standard container. Your IT team points it at your internal AI gateway and your own database, and it runs entirely inside your environment — no data transmitted externally at any point.
What you'll need
- ·An internal AI endpoint (Azure OpenAI or a compatible model gateway)
- ·A Postgres-compatible database
No proprietary dependencies, no vendor lock-in, no ongoing calls to external services.
What your team gets
Once deployed, your team gets a structured, repeatable way to assess AI data readiness — across teams, departments, or the whole organization, with results stored internally, shareable, and consistent enough to track progress over time.
Statistically Defensible Methodology
ADM's sampling approach was developed in consultation with Bruce Ratner, PhD, Predictive Analytics Consultant. It uses stratified random sampling with dynamic sizing (Cochran formula, 95% confidence, ±5% margin of error) and forced outlier inclusion — making findings defensible for enterprise data governance and compliance contexts.
Data Governance Built In
ADM isn't just an AI readiness tool — it's a data governance assessment. Every assessment produces an ADM Data Dictionary your team can use for documentation, onboarding, and compliance. PII detection is powered by Google Cloud Sensitive Data Protection, catching semantic PII — names, addresses, dates of birth — that regex patterns miss. For enterprise deployments, DLP is designed to run inside your own GCP environment.
Extend Governance Beyond the Warehouse
Traditional data governance platforms protect your core systems. ADM fills the gap — bringing instant governance assessment to ad-hoc files, vendor extracts, analyst exports, and any dataset that lives outside your enterprise catalog. No IT ticket. No onboarding. Results in under two minutes.
Proprietary Deep Dive Analysis
ADM's column intelligence goes beyond column names. For every column in your dataset, our Deep Dive Analysis examines actual data values to determine true semantic meaning — resolving ambiguity where possible and producing AI Context descriptions that are specific, accurate, and actionable. Combined with our statistically defensible sampling methodology, this gives enterprise users a data assessment they can stand behind.
Interested in a pilot?
Let's talk. We'll figure out if it's a fit.