Is Your Data Ready for AI?
Upload a data file and get an AI Readiness Score and a column-by-column assessment.
AI can misinterpret data that is missing, ambiguous, or mislabeled.
The score is just the beginning ↓What You Get
One score. Three risks.
Hallucination risk. Sensitive data exposure. Data quality issues. We combine all three into a single score — so you know, at a glance, whether this dataset is safe to connect to AI, or what still needs attention first.
AI Data Maturity Score
What You Get
Clear data definitions so AI can't guess wrong
We read the column names, then compare them against the actual data — and produce a plain-English definition of exactly what each column contains. This is the semantic layer AI needs to stop guessing. No other tool builds it this way.
Sample Database Demo for CRM Salesforce Test — each row represents a sales opportunity.
- opp_id:Unique identifier for sales opportunities.
- acct_nm:Name of the account associated with the opportunity.
- amt:Monetary amount associated with the opportunity.
- close_dt:Projected close date for the opportunity.
- terr:Abbreviation for the geographical region of the opportunity — may require a lookup to interpret.
- rtg:Rating of lead temperature (e.g. hot/warm/cold) indicating engagement level — not a literal temperature measure.
What You Get
A quick on-ramp to AI-powered analysis
Five real questions this specific dataset can actually answer, ready to paste into any AI tool. Start asking the right questions — without wasting time figuring out where to start.
Starter Prompts
Sample Database Demo — crm_salesforce_test — paste into your LLM of choice
DATASET CONTEXT: This dataset contains sales opportunity records, with each row representing an individual sales opportunity. Calculate the total monetary value of sales opportunities grouped by each sales representative. Normalize the values in the 'Status' column to ensure consistency in categorization.
DATASET CONTEXT: This dataset contains sales opportunity records, with each row representing an individual sales opportunity. Determine the average number of days spent in each stage of the sales process, categorized by the current status of the opportunities. Note: 'days_in_stg' has a high null rate — results reflect only rows where data was recorded.
DATASET CONTEXT: This dataset contains sales opportunity records, with each row representing an individual sales opportunity. Count the number of sales opportunities for each lead source. Ensure that the 'lead_src' column values are consistent for accurate grouping.
Each prompt includes your Dataset Briefing and relevant data caveats.
What You Get
A specific, ordered checklist — before you connect anything
Resolve ambiguous column names. Mask columns with sensitive data. Review columns with missing values. Concrete actions, in order, so you know exactly what to fix before AI has the chance to guess for you.
Resolve Ambiguous Column Names
src_cd, cmpgn, flg — rename or document before connecting
Assess Null Impact
revenue and cmpgn have significant null rates
Mask or Remove Sensitive Data
email detected — mask before connecting to AI
Want to see it in action?
Our Process
Your sensitive data is never exposed during assessment
Before any assessment takes place, all sensitive data — names, emails, phone numbers, health and financial details — is masked with special tokens that replace the real values. AI never sees any sensitive information, so the assessment stays valid without ever risking your data.
Your file
Raw data
Layer 1 — Regex masking
Email, SSN, phone, credit card
Layer 2 — Google Cloud DLP
Names, addresses, DOB, sensitive free-text data
AI analysis on clean data
Original values never reach AI
Our Process
We only take a sample of your data
We use approved statistical methods to take a stratified sample that provides 95% confidence, ±5% margin of error, to perform the assessment. Your full file is never seen by AI — which limits risk, boosts performance, and still delivers a highly accurate assessment. All files are deleted immediately.
Your file
Uploaded securely
Statistical sample taken
Cochran formula, 95% confidence ±5% margin of error
Sample processed on Google Cloud
Never stored, never shared
Assessment complete
Data deleted, nothing retained
What You Get
Value beyond the report
A complete Excel data dictionary — ready to use for documentation, data governance, and as a checklist for remediation. It fills a real gap: companies without a formal governance tool get one instantly, and large enterprises get visibility into the shadow data and critical files that live in spreadsheets, outside any governance tool's reach.
| Column | Type | Quality | Ambiguous | Rename To | Description |
|---|---|---|---|---|---|
| session_id | text | 10 | — | — | Unique session identifier |
| src_cd | text | 9 | Yes | source_channel | Marketing acquisition source |
| cmpgn | text | 8 | Yes | campaign_name | Campaign identifier |
| text | 10 | — | — | User email address (sensitive data) | |
| revenue | numeric | 6 | — | — | Session revenue amount |
Know your risk before AI does.
Free. No account required. Results in under two minutes.
Works with any dataset — marketing analytics, sales data, financial records, healthcare, supply chain, HR files, and more.
Upload My Data →