AI Data Integrity Checker
Detect errors and inconsistencies in your data fast
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When was the last time anyone checked whether your main spreadsheet still makes sense? Do the totals in it agree with the rows above them? And if two columns contradicted each other, how long would it take anybody to notice?
Data rarely fails loudly. It drifts. A field changes meaning, someone types text into a numeric column, a date arrives in the wrong century, and everything downstream keeps working while producing answers that are quietly wrong.
Short answer: The AI Data Integrity Checker reviews a dataset for missing values, type mismatches, contradictions, outliers and broken relationships, then reports what it found with a severity for each. Paste the data and read the assessment. Free, with no account needed.
What is AI Data Integrity Checker?
The AI Data Integrity Checker examines a set of records and reports on their internal health. It looks for the things that make data untrustworthy rather than the things that make it invalid.
That distinction matters. A validator asks whether each field is correctly formed. An integrity check asks whether the dataset makes sense as a whole. A date of birth in 2027 is a perfectly valid date. An order with a delivery date before its order date has two valid dates. A customer appearing twice with different phone numbers has two valid phone numbers. None of those fail a field level check, and all of them are problems.
Why Use AI Data Integrity Checker?
- Contradictions surface. Fields that disagree with each other are found, not just fields that are malformed.
- Completeness gets measured. You see which columns have gaps and how serious they are.
- Outliers are questioned. Values far outside the normal range are raised for a human decision.
- Severity is assigned. Findings arrive ranked, so you fix what matters first.
- No schema needed. It works on data you were handed, without a specification to check against.
Who Should Use It?
- Analysts assessing a dataset before building anything on top of it
- Operations teams auditing a system of record that several people edit
- Developers checking a migration before and after it runs
- Researchers reviewing collected data before analysis begins
- Anyone inheriting a spreadsheet that a business depends on
How Does AI Data Integrity Checker Work?
The tool uses the working surface shared across the site, and runs in a single pass.
Your records go into the prompt input area, which shows "Paste or describe what you want evaluated for the data integrity checker…". Paste a sample of rows including the header. The AI model selector below offers the engine, holding Anthropic Claude AI, NVIDIA AI and DeepSeek among several more, including MSB AI, Google Gemini and MiniMax.
The advanced options accordion stays collapsed until you open it, and this panel is genuinely about the checking rather than only the presentation. Generate sends the data, the engine and your settings through the prompt engineering layer, which is the prepared instruction set behind this tool.
The output section returns the assessment in a result card with a live word count in its footer. The export tools row offers DOC, TXT and HTML, plus Copy, Listen, Reuse, Download and full view. The activity history panel keeps the session's runs, which lets you compare a lenient pass against a strict one on the same data.
Step-by-Step Guide
Audit a dataset in the AI Data Integrity Checker.
- Take a representative sample of rows, including the header line.
- Paste it into the prompt box.
- Explain what the data represents, since context is what makes a contradiction visible.
- Set Evaluation Criteria to Completeness for a first pass, then run again on Accuracy.
- Set Strictness to Standard to begin with.
- Turn List Improvements on so each finding comes with a suggested fix.
- Generate, then fix the highest severity findings before re running.
Before you trust a dataset you have cleaned:
- ✅ Every finding marked as high severity has been resolved or accepted deliberately
- ✅ Missing values were investigated rather than filled with a default
- ✅ Outliers were confirmed as real before being kept or removed
- ✅ Date fields were checked for impossible sequences
- ✅ Duplicate records were merged with a rule about which version wins
- ✅ You re ran the check after fixing, since fixes introduce their own errors
Key Features
Cross field logic
Finds values that are individually valid and impossible together, such as a delivery before its order.
Completeness profile
Shows which columns have gaps and whether the gaps follow a pattern worth investigating.
Type consistency
Catches columns where numbers, text and dates have been mixed into the same field.
Adjustable strictness
Run lenient for a quick overview or very strict when the data feeds something important.
Suggested fixes
Each finding can arrive with a recommended action rather than only a description.
Best Use Cases
| Moment | What to check for | Strictness to use |
|---|---|---|
| Inheriting a dataset | Overall quality and obvious contradictions | Standard, for an honest first picture |
| Before a migration | Completeness and type consistency | Strict, since import errors are expensive |
| After a migration | Whether anything changed in transit | Very Strict, comparing against the source |
| Before reporting | Outliers and impossible values | Strict, because these distort every total |
Advanced Options Guide
Ten controls sit in the accordion, and this panel shapes the assessment itself rather than only its presentation.
| Option | What it controls | When to change it | Suggested starting point |
|---|---|---|---|
| Evaluation Criteria | What the check focuses on: Overall, Quality, Accuracy, Completeness, Strengths, Weaknesses, Readiness or Compliance. | Run it more than once with different criteria. Each surfaces different problems. | Completeness first, then Accuracy |
| Strictness | How readily something is treated as a problem: Lenient, Standard, Strict or Very Strict. | Very Strict before a migration, Lenient for a quick overview of unfamiliar data. | Standard |
| Output Format | How findings are presented: Score + Feedback, Detailed Report, Checklist, Strengths / Improvements or Rubric. | Checklist when the output becomes a work list for someone. | Detailed Report |
| Feedback Style | The manner of the assessment: Constructive, Direct, Detailed, Encouraging or Actionable. | Actionable when you want fixes rather than descriptions. | Actionable |
| Give a Score | On and off toggle adding an overall quality score. | On when tracking improvement across repeated runs. | On |
| List Strengths | On and off toggle noting what the dataset does well. | On, since knowing which columns are reliable is genuinely useful. | On |
| List Improvements | On and off toggle listing what to fix. | Always on. This is the part you will act on. | On |
| Provide Examples | On and off toggle citing specific offending rows. | On, because a finding without an example is hard to act on. | On |
| Strictness Level | Slider from 1 to 100 giving finer control than the dropdown. | Use it to tune between two dropdown settings that both feel wrong. | Around 60 |
| Custom Instructions | Free text up to 1000 characters, placeholder "Add any extra instructions, context, or preferences…". | What the data means, the rules it must obey, and which columns matter most. | Try: "Orders data. Delivery date must follow order date. Quantity must be positive. Email is required." |
Example Inputs
Five order rows where every individual field is well formed:
order_id,customer,order_date,delivery_date,quantity,total
1001,J Patel,2026-03-02,2026-03-05,2,48.00
1002,A Novak,2026-03-03,2026-02-28,1,24.00
1003,J Patel,2026-03-04,2026-03-08,0,24.00
1004,L Moreau,2026-03-05,2026-03-09,3,7200.00
1005,,2026-03-06,2026-03-10,1,24.00Nothing there would fail a format check, and four of the five rows have a problem. Order 1002 is delivered before it was placed. Order 1003 has a quantity of zero yet a total of 24.00. Order 1004 is priced at roughly a hundred times the going rate, which may be genuine or may be a decimal in the wrong place. Order 1005 has no customer at all. Only 1001 is clean.
Valid is not the same as correct Field level validation would pass every row above. Integrity checking is what finds problems that only exist in the relationship between fields, and those are the ones that corrupt reports rather than crashing imports.
Written out as findings, that sample produces a report along these lines:
| Row | Finding | Severity |
|---|---|---|
| 1002 | Delivery date precedes the order date | High, the record is impossible |
| 1003 | Quantity of zero with a non zero total | High, one of the two fields is wrong |
| 1005 | Customer name missing | Medium, the order cannot be attributed |
| 1004 | Total far above the range of other rows | Low, flagged for confirmation |
Explain what the data is The tool cannot know that a delivery must follow an order unless you say so or the column names make it obvious. One sentence of context in Custom Instructions roughly doubles what a check can find.
Run it twice, differently A pass on Completeness and a pass on Accuracy find different things. Two runs at Standard strictness reveal more than one run at Very Strict, and produce far less noise.
What works well
- Finds contradictions between fields that pass individually
- Ranks findings so the important ones get attention first
- Needs no schema, so it works on data you were simply handed
- Suggests fixes rather than only listing faults
What to watch for
- It works on the sample you paste, so the sample must be representative
- Outliers may be genuine, and need a person to confirm
- Without context it can only find problems visible in the data itself
AIToolsay is a free AI platform of purpose built tools, each with its own options panel and its own prompt engineering, rather than one general chat box under many names. No registration is required anywhere, and eleven engine families sit in a single menu, so a strict reviewer and a lenient one are both available. The AIToolsay homepage also carries AI courses and curated collections, worth browsing if data quality work is becoming a regular job. For field level rules the AI Data Validator is the complement to this one, and once the data is trustworthy the AI Data Analysis Assistant is where the actual questions get asked.
Frequently Asked Questions
Is the AI Data Integrity Checker free?
Yes, with no account and no limit on how many checks you run.
How is this different from a validator?
A validator checks each field on its own. This checks whether the record makes sense as a whole, which catches contradictions between fields that are individually correct.
How much data should I paste?
A representative sample rather than everything. A few hundred rows that reflect the real variety in your data will surface the patterns worth knowing about.
Does it need to know my schema?
No, but context helps enormously. Explaining what the data represents and any rules it must follow lets the check find problems it could not otherwise see.
Should I use the strictest setting?
Not at first. Very Strict produces a great deal of noise on real data. Start at Standard, fix what it finds, then raise the strictness for a second pass.
Will it fix the problems for me?
It suggests fixes. Applying them is deliberately left to you, because the right correction usually depends on knowledge of the business that the data does not contain.
Data quality work is unglamorous and it compounds. Every contradiction left in place becomes an answer someone acts on later. Take a sample, explain what it is, fix the serious findings, and run it again. Two passes will tell you more about a dataset than a week of assuming it is fine.
Thank you for reading, and I hope your next audit turns up less than you feared. If this is useful, join the AIToolsay community, follow AIToolsay on social media, turn on push notifications for new tools, and subscribe to the newsletter for the email roundup.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
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