How It Works
A column-by-column AI readiness assessment in four steps.
Step 01
Upload Your Dataset
Upload any CSV or Excel file. No account needed, no data stored. Before any AI analysis begins, we automatically scan and mask PII in your data.
Step 02
Column-by-Column Analysis
Every column is assessed for data quality, naming clarity, PII/HIPAA risk, and how likely AI is to misinterpret it. Results arrive in under a minute.
Step 03
Your AI Data Maturity Score
A score from 0–100 tells you exactly where your dataset stands — from Not AI Ready to AI Ready — with specific findings for every column.
Step 04
Your AI Context
The most valuable output: a ready-to-use paragraph you paste before any AI query on this dataset. It tells the AI what your data means, what to watch out for, and how to interpret ambiguous fields.
Step 05
Your Starter Prompts
Generate starter prompts built from your specific dataset. Each prompt includes your AI Context and relevant data caveats — ready to paste into any AI tool. No prompt engineering needed.
Why nulls matter more for AI than traditional reporting
In traditional reporting, analysts know to handle nulls explicitly — they write WHERE clauses and aggregate functions with null awareness. AI analytics tools like Databricks Genie and Microsoft Copilot generate queries automatically. When they encounter a column with 30% null values, they may silently drop those rows or return an incomplete result — with no visible warning.
The AI Data Maturity context block fixes this. For columns with significant nulls, it tells the AI exactly what null means in that column and how to handle it — so queries are written correctly the first time.
How We Sample Your Data
Rather than scanning your entire file, ADM analyzes a statistically representative sample — large enough to identify data quality patterns with confidence, small enough to deliver results in seconds.
We use a three-part methodology:
Stratified random sampling
Rows are drawn proportionally across data categories, ensuring no segment is over or underrepresented.
Dynamic sample sizing
Sample size is calculated using the Cochran formula at 95% confidence and ±5% margin of error, scaled to your dataset size.
Outlier inclusion
Minimum and maximum values from numeric columns are always included, ensuring edge cases are never missed.
Sampling methodology developed in consultation with Bruce Ratner, PhD, Predictive Analytics Consultant.
How We Handle Your Data
Column statistics go to AI — not your data
We send column names, data types, null rates, and sample values to OpenAI for analysis. Before transmission, we automatically scan your data for PII — including email addresses, phone numbers, Social Security numbers, and credit card numbers — and mask detected values with placeholder tokens (e.g. [EMAIL], [PHONE]). OpenAI never sees your original PII values. This masking step runs on our servers before AI analysis begins.
Assessment accuracy
Results are directionally accurate for identifying data quality and readiness issues. The AI Data Maturity Score is a practical assessment guide — not a formal statistical audit. For datasets where precision matters, we recommend reviewing flagged columns manually.
The AI Data Maturity Assessment is completely free — no account, no credit card, no catch.