AI Data Validator
Validate data integrity and catch errors automatically
NVIDIA: Nemotron 3 Super
Balanced Nemotron for demanding everyday work
NEW
FREE
Your prompt will appear here…
Your beautifully formatted article will appear here once you generate.
No history yet
Your generations will appear here. Sign in to save them permanently.
Who receives the file after you send it? A system, usually, and it will accept everything you give it right up to the point where it cannot. AI Data Validator is the check between those two moments, run while you can still do something about what it finds.
Short answer: AI Data Validator checks data you paste against rules you describe and reports what fails. Missing values, wrong formats, out of range figures, broken references and duplicate identifiers are all reported with the rows they occur in.
What is AI Data Validator?
AI Data Validator is a free browser tool that checks data against expectations. The prompt box asks for your topic, details or requirements, and what goes in is the data plus the rules it is supposed to satisfy.
That second half matters more than it sounds. Validation without stated rules degenerates into a general impression that the data looks fine. Validation with rules produces a list of specific failures in specific rows, which is the only output anybody can act on.
Why Use AI Data Validator?
Because the alternative is discovering the problems during an import, one at a time, in the order the importer happens to encounter them. A validation pass finds all of them at once, which turns six rounds of upload and rejection into a single fix.
The second reason is that many rules never get written down anywhere. Everyone knows the reference number has eight characters and that the region must be one of five values, and nobody has recorded it. Stating those rules to run a check is also the act of documenting them.
What works well
- All failures at once rather than one per attempt.
- Rules can be described in words rather than expressed as code.
- Reports the row, so each failure is findable.
- Free, so validating before every send costs nothing.
What to watch for
- It checks the rules you state, so an unstated rule is an unchecked rule.
- It is not the importer, so its pass is not a guarantee of acceptance.
- Strip personal values before pasting anything from a live system.
Important A clean validation report means your stated rules passed. It does not mean the destination will accept the file. Treat it as the check before the check, not as a substitute for the real one.
Who Should Use It?
Anyone preparing a file for a system they do not control. Operations teams doing bulk uploads. Finance teams submitting returns. Marketers importing contact lists. Developers checking fixtures. Anyone who has had an import rejected twice and would like the third attempt to be the last.
| Who | What usually fails | Rule to state |
|---|---|---|
| Operations | Required fields left blank | Which fields may never be empty |
| Finance | Figures in the wrong format or unit | Decimal places, currency and sign conventions |
| Marketer | Malformed addresses and duplicates | Format rules and what makes a record unique |
| Developer | References pointing at nothing | Which fields must match a value elsewhere |
How Does AI Data Validator Work?
The tool sits on one page. Its prompt box carries the placeholder Enter your topic, details, or requirements for the data validator, and both the data and the rules go in there. Below it the model row lets you choose an engine: Anthropic Claude AI and OpenRouter AI both sit there, and the rest of the row is one click away. The advanced options accordion and the generate button follow.
- Write your rules out, one per line, before pasting anything.
- Replace personal values with placeholders.
- Paste the rules and then the data.
- Ask for the row number against each failure.
- Generate, fix the systematic faults first, then the individual ones.
- Validate again, because fixes introduce errors of their own.
The report lands in a card with a DOC, TXT and HTML export row attached, which matters when the failures have to go back to whoever produced the data. Copy, listen, reuse, download and open in full view sit alongside, and the session history panel keeps every validation from the session listed below, which is how a before and after pair stays comparable.
Key Features
Rules in plain words
Constraints described in a sentence rather than expressed as a schema, which is what makes this usable without writing code.
Row level reporting
Each failure comes with the row it occurred in, so fixing is a lookup rather than a search.
Duplicate detection
Checks uniqueness on the fields you say identify a record, which is not always the ones the file calls an id.
Reference checks
Confirms that values which should match something elsewhere actually do, which catches the failures an import finds last.
Grouped output
Failures organised by rule rather than by row, so a single systematic fault is obvious instead of appearing forty times.
Best Use Cases
Checking a bulk upload before submitting it. Validating a supplier's file before it enters your systems. Confirming a migration export before the cutover. Checking a form export where users typed values freely. Confirming that a generated file meets the specification it was written to. For single field checks at scale, the specialised tools are a better fit, such as the AI Email Validator for address lists.
| Rule type | What it catches | How to state it |
|---|---|---|
| Presence | Required fields left blank | Name the fields that may never be empty |
| Format | Values shaped wrongly | Give the pattern or an example of a correct one |
| Range | Figures outside what is possible | State the minimum, maximum and sign |
| Uniqueness | Two records that should be one | Say which fields together identify a record |
Advanced Options Guide
These are optional controls that change more than the wording of your prompt does. The dropdowns decide how the report reads and Custom Instructions carries the rules themselves, which is where nearly all the value sits.
| Option | What it controls | When to change it | Starting point |
|---|---|---|---|
| Output Type | Standard, Detailed, Concise, Structured, Template, Step by Step, Professional or Creative | Structured when the report goes back to a data producer | Structured |
| Tone / Style | Professional, Formal, Friendly, Simple, Academic, Persuasive, Confident or Neutral | Friendly when the report goes to a colleague who made the errors | Neutral |
| Length | Short, Normal, Long or Detailed | Detailed when every failing row must be listed individually | Normal |
| Focus / Audience | General, Writers, Students, Professionals, Developers, Marketers, Researchers or Everyday Use | Developers for a technical report, Everyday Use when the fixer is not technical | Professionals |
| Include Examples | Shows a failing value alongside each rule | Leave on, an example is what makes a rule violation obvious | On |
| Use Clear Structure | Groups failures by rule | On, since it reveals systematic faults | On |
| Include Key Points | Adds a summary of how many rows failed and why | On for anything over a few dozen rows | On |
| Keep It Concise | Trims explanation to the failures alone | On when you know the rules and just want the list | Off first time |
| Detail Level | Slider from 1 to 100 controlling how much each failure is explained | Higher when somebody else has to fix the data | Around 50 |
| Custom Instructions | Free text up to 1000 characters | The rules live here, one per line | Try "id must be 8 characters, email must contain @, region from the list, no duplicate ids, total must be positive" |
Tip Write one rule per line rather than a paragraph. Rules in a list get checked individually and reported individually; rules in a paragraph tend to be summarised into a general impression.
Example Inputs
Rules first, data second, is the pattern that works. A realistic paste into the AI Data Validator:
Check these rules. Report the row for each failure.
- ref must be exactly 8 characters
- email must contain @ and a dot after it
- region must be one of: NW, SE, MID, WA
- ref must be unique
- total must be a positive number with two decimals
ref,email,region,total
AB123456,priya@example.com,NW,249.00
AB12345,tom@example.com,SE,1100.00
AB123457,ana.example.com,MID,89.5
AB123456,jo@example.com,WAL,-40.00
AB123458,,NW,0.00Every row after the first breaks at least one rule, and one of them breaks three. That is not unrealistic for a file assembled by hand under time pressure.
Example Outputs
With rules stated, examples on and failures grouped by rule, the report came back as:
5 rows checked. 4 rows failed. 7 failures across 5 rules.
RULE: ref must be exactly 8 characters
Row 3 "AB12345" is 7 characters
RULE: email must contain @ and a dot after it
Row 4 "ana.example.com" no @ present
Row 6 "" empty
RULE: region must be one of NW, SE, MID, WA
Row 5 "WAL" not in the allowed list. Closest: WA
RULE: ref must be unique
Rows 2 and 5 both use "AB123456"
RULE: total must be a positive number with two decimals
Row 4 "89.5" one decimal place
Row 5 "-40.00" negative
MOST LIKELY SYSTEMATIC
Nothing. All seven failures look like individual entry
errors rather than one repeated fault.The closing judgement is worth asking for. Seven individual mistakes need seven fixes; seven instances of one systematic fault need one fix upstream, and knowing which situation you are in changes what you do next.
Tips & Common Mistakes
Validation goes wrong for reasons that are almost always about the rules rather than the data.
- ✅ Write one rule per line
- ✅ State allowed value lists explicitly rather than describing them
- ✅ Say which fields make a record unique
- ✅ Ask whether the failures look systematic or individual
- ✅ Validate again after fixing, since fixes introduce their own errors
Pro tip Keep your rule list in a file and paste it every time. After two or three months it becomes the written specification for your data that nobody ever got round to producing, and it is more accurate than one written from memory would have been.
AIToolsay is where the neighbouring tools are grouped, and validation sits in the middle of a chain. Data gets cleaned and standardised before it, and imported, converted or analysed after it, and a failed validation usually sends you back a step. Each has a purpose built tool, and one shared shell keeps everything familiar: the same prompt box, the same model row, the same options accordion, the same export controls, the same session history panel. Free and open at each step, with output that fits the next tool without any rework.
Frequently Asked Questions
Is AI Data Validator free?
Yes, and nothing about it is limited: not the options, not the exports, not the runs.
Does a clean report guarantee my import will work?
No. It means your stated rules passed. The destination may enforce rules you did not state, which is why the report is the check before the real one.
How do I express the rules?
In plain sentences, one per line. Allowed value lists should be written out rather than described, since a described list is an opinion and a written one is a rule.
Can it check duplicates?
Yes, on whichever fields you say identify a record. That is often a combination rather than the single column labelled id.
Will it fix the failures?
It reports them. Fixing is a separate decision, and it should be, since some failures are data errors and others mean your rule was wrong.
How much data can it check?
A few hundred rows comfortably. For larger files, validate a sample to find the rule violations, then apply the same rules with a script.
State the rules explicitly, one per line, and ask whether the failures are systematic. Most of the time the answer to that single question determines whether you spend ten minutes fixing rows or five minutes fixing the thing that produced them.
Thank you for reading. Telegram announcements come first, push notifications reach you for anything substantial, and the newsletter gathers a month of releases in one email. The rest of the catalogue is at AIToolsay.
Let AI Speak.
Sabir
Founder of AIToolsay and a passionate AI enthusiast dedicated to building practical, user-friendly AI tools that simplify everyday tasks.
Can't find the tool you need?
Tell us what you need and we will build it. New tools ship every week.
Request a Tool