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SNOWFLAKE NATIVE APP | NAME VALIDATION 3
Validate and understand names in Snowflake
Validate, classify, and parse names right where they live — no exports, no pipelines. Every row comes back with a confidence score and a clear classification, not a pass or fail.
Billions of name pairs
80 cultures
Zero data retention
Name in → structured out
In your warehouse
박민준
FullName · 0.98Park Min-Jun
Michael's Diamonds
Business · 0.94Not a person
xX_asdf_Xx
Garbage · 0.03Leet flagged
One SQL call. Results land back in a Snowflake table.
Trusted by data teams at brands that can't afford bad records
The problem
Not everything in a name field is a name.
Every form, import, and third-party feed that writes to a name column writes other things to it too. The column ends up holding real names, business names, partial names, and deliberate junk — and none of it errors out.
Business names in person fields
A company registered where an individual was expected, so the record fails person-level matching.
Structure problems
First and last swapped, two people merged into one field, a suffix parsed as a surname.
Script and culture mismatches
Non-Latin characters and naming patterns arriving in systems that assume a Western first-middle-last shape.
Deliberate junk
Test entries, keyboard mashing, celebrity names, insults, and leet-speak spellings that survive into production.
A parser can split the string. It can't tell you whether the input is a name at all, what kind of name it is, or how much to trust the answer.
What you get back
Eight signals for every name — not one flag.
Behind each result is an ensemble of machine-learning models trained on billions of name pairs across 80 cultures. Every field lands in a Snowflake table, ready to route, correct, enrich, or quarantine in SQL.
Validity & confidence
A valid-name indicator plus a continuous 0-to-1 confidence score.
Classification
FullName, FirstName, LastName, DuoName, Business, Dictionary, Garbage, or Unknown.
Parsed components
Prefix, first, middle, last, and suffix — separated out of one input string.
Standardized names
Corrected, normalized forms ready for downstream matching.
Person vs. business
Tells you when a company landed in a field built for a person.
Global name handling
Translation and transliteration for non-Latin scripts and non-Western patterns.
Quality signals
Bogus, vulgar, celebrity, dictionary, garbage, and negative-sentiment flags — including disguised leet-speak.
Notes & warnings
The context behind the result, for records where the score alone isn't enough.
Similar & related names
Extra matches to feed dedup and entity resolution — all in the same output row.
The shift
Stop asking valid or invalid. Start asking how sure — and what kind.
A yes/no answer forces someone else's threshold onto your data. A graded score hands the decision back to you.
The old way
A rule or a lookup list
"INVALID"
- Rejects unusual real names it has never seen
- Accepts ordinary-looking fakes
- No reason, no confidence, no middle ground
- Needs maintenance every time your data shifts
With NV3
A model that scores and explains
0.94 · FullName
- A continuous 0–1 confidence score you set thresholds on
- An eight-class classification, not just a flag
- Scores names it has never encountered
- Accept, review, or reject — change the lines, not the code
How it works
Live in three steps. All inside Snowflake.
1
Install the native app
Get it from the Snowflake Marketplace and grant the external access integration. It runs inside your account — nothing to stand up, no API client to build.
2
Point it at your table
Pass a SELECT and an output table path — or bind a source table once and call the procedure with no arguments.
3
Read the results
A table lands in your schema with the full output for every row. Join it back on the row ID and decide what to do in SQL.
worksheet.sqlSQL
-- Validate a whole table -- (bind it once, then call with no arguments) CALL APP_CODE.NV3_VALIDATENAME(); -- Or validate specific records with a query CALL APP_CODE.NV3_VALIDATENAME( 'SELECT CONSUMER_ROW_ID, FULLNAME FROM MY_SCHEMA.MY_TABLE' ); -- Or write results to a specific output table CALL APP_CODE.NV3_VALIDATENAME( 'SELECT CONSUMER_ROW_ID, FULLNAME FROM MY_SCHEMA.MY_TABLE', 'MY_SCHEMA.NV3_RESULTS' );
Install, call, use — results land back in your own table.
No batch files. No CSV export. No third-party bucket. No API integration to maintain.
Who it's for
Built for the teams that inherit the name field.
Data quality & governance
Segment a messy customer table and quantify the problem with classification and confidence.
Onboarding & KYC
Route questionable names to review before they propagate, using thresholds and quality signals.
Identity resolution & MDM
Feed the match engine cleaner inputs: parsed components, standardized forms, person vs. business.
Fraud & risk operations
Catch bogus, vulgar, celebrity, garbage, and negative-sentiment entries — even disguised leet-speak.
Analytics & ML engineering
Turn raw name strings into standardized, model-ready components and classifications.
Marketing & contact ops
Parse names and catch businesses before personalization goes wrong in public.
Security & architecture
You control the connection. We retain nothing.
NV3 runs as a native app inside your Snowflake account. When you call it, the app sends name data to the Service Objects API through a single external access integration that you explicitly grant at setup. The API validates the name, returns the result, and retains nothing. Your data stays private and secure because it never sits in a third-party system and nothing is kept on our side.
The external access integration is the only connection. It is fully visible in the app manifest before you install, you grant it yourself, and you can revoke it at any time. There are no batch files in transit, no data sitting in a third-party bucket, and no CSV exports leaving your environment.
SOC 2 Type II
PCI-DSS
Your Snowflake accountThe app runs on your data in your warehouse
Single external access integrationYou grant it, you can revoke it — the only connection
Service Objects validation APIValidates & returns the result — stores nothing
Why Service Objects
Enterprise reliability, proven since 2001.
The validation infrastructure behind NV3 has run in production for over two decades — trusted by Fortune 500 data teams and backed by billions of validated transactions.
25 Years
In production, since 2001
8 Billion+
Validations delivered
99.999%
Uptime SLA, financially backed
SOC 2 Type II
Independently audited security
Fortune 500
Clients rely on our data
Pricing
See the price before you talk to anyone.
Both tiers return the complete output, same latency, same uptime SLA. The only difference is volume.
Standard
$3,500 / year
100,000 validations
- Full output — confidence, classification, parsed & standardized fields
- Sub-500ms latency, 99.999% uptime
- 20-day trial — 1,000 records, full output
Higher volume
Enterprise
$7,500 / year
300,000 validations
- Full output — confidence, classification, parsed & standardized fields
- Sub-500ms latency, 99.999% uptime
- 20-day trial — 1,000 records, full output
Marketplace Capacity Drawdown (MCD) supported.
Apply a percentage of your committed Snowflake capacity toward this purchase. No separate procurement process and no additional contract — if your team already has committed Snowflake spend, this draws against it.
No "request a quote." No sales call required to learn what it costs.
Run it on your own name data.
Free for 20 days.
1,000 validations with the complete output: confidence scores, classifications, parsed components, and quality signals. Install from the Snowflake Marketplace, grant the connection, and validate your first batch in minutes.
The trial stops at 20 days or 1,000 records, whichever comes first. No auto-charge and no surprise conversion.
Questions, answered
Frequently asked questions
What is actually doing the validation?
An ensemble of machine-learning models, each evaluating a different aspect of the input, combined with authoritative name datasets and validation rules. It is not a lookup against a list of known first names — which is why it can score a name it has never seen and flag a common-looking name being used as junk.
Does it work on names outside the US?
Yes. The engine is built on billions of name pairs across 80 cultures, and the output includes translation and transliteration for non-Latin scripts. (This differs from the address validation app, which is US only.)
Can it tell a person from a company?
Yes. Person versus business is part of the eight-class classification, so a company name that landed in a person field is identified rather than scored as a bad person name.
Does it detect fraud?
No. It validates the name, it does not score the person. It gives you a signal — junk, bogus, vulgar, celebrity, dictionary, and intentionally negative inputs are flagged, including disguised leet-speak. How that signal feeds a risk decision is your model's call, not this app's.
Does my data leave Snowflake?
The app runs inside your Snowflake account and sends name data to the Service Objects API through a single external access integration you grant and revoke. That traffic is encrypted in transit over HTTPS, and nothing is retained after the response returns. No batch file, no third-party bucket, no standing credential outside your control.
Is this real-time?
It runs in your warehouse against tables, on demand or on a schedule. It is not a point-of-entry check embedded in your signup form.
What's the difference between Standard and Enterprise?
Volume only. Both tiers return the complete output with the same latency and the same uptime SLA.
Can we pay with our Snowflake commitment?
Yes. Marketplace Capacity Drawdown lets you apply a percentage of your committed Snowflake capacity to this purchase, so it doesn't require a separate contract or vendor onboarding cycle.
How is this different from writing our own cleanup rules?
Rules give you a binary answer from a fixed list. They reject unusual real names, accept ordinary-looking fakes, and need maintenance every time your data changes. This returns a graded score with a classification and a reason, so you set the thresholds and the engine does the recognition.
The rest of the portfolio
Clean names are half the record.
The address on that same contact record has the same problem. Validate and enrich it in the same Snowflake account.