Missing piece of your text data quality is here.Start monitoring semantic quality with Qualantic.
Text data correctness can’t rely only on rule-based or aggregate checks. Qualantic scans your text columns record by record at scale and points you to the rows that shouldn’t exist in production.
The records your checks miss
| Column | Value | What's Wrong |
|---|---|---|
| job_title | 01/2019 - Present | Date range parsed as job title |
| first_name | THC Pharmacy | Organization, not a person |
| company_name | John Smith | Person name in company field |
| job_title | Classification Cutsoms Brokarage | Garbled / spelling errors |
| first_name | Nephrology | Medical specialty, not a name |
| region | United States | Country in a region field |
These records pass null checks. They pass schema validation. They pass freshness monitoring. They exist in your production database right now.
Cleaning executive title data at a B2B data provider
A B2B data provider wanted to validate the quality of their executive-level job title data. They had 1.7 million unique titles tagged as senior leadership. Schema checks, null checks, and distribution monitoring showed everything was clean.
A single Qualantic scan grouped similar records into clusters— groups of titles that share a pattern — and flagged the suspicious ones. An LLM verified each cluster, and the top 200 most suspicious were sent to domain experts for final review.
The result: 199 out of 200 clusters required action. Only one turned out to be clean data.
What researchers confirmed
76% Wrong leadership tags
Real job titles that didn’t belong in the executive category. Tags corrected in bulk — 80,000 records updated.
18% Needed row-by-row review
Mixed clusters where some records were valid and others weren’t. Sent to domain experts for individual classification.
6% Not job titles at all
Dates, word fragments, descriptions — data that should never have been in a title field. 6,000 records removed.
Examples of what was found
| Column | Value found | What's wrong |
|---|---|---|
| job_title | Chief 2019 | A year, not a title |
| job_title | Presents | Word fragment, not a role |
| job_title | Booster Club President | Volunteer role, not corporate title |
| job_title | Founding Team. Sales and Partnerships | Role description, not a title |
| job_title | President of Physics and Astronomy Club | Student club — detected via company context |
“President of Physics and Astronomy Club” looks like a valid title in isolation. The system flagged it because the company field showed a university — cross-column context reveals what single-column checks miss.
These records passed null checks, schema validation, and freshness monitoring. They were in production for years.
Findings verified by domain experts. Only 1 out of 200 clusters required no action.
That was just the first batch.
After reviewing the results, the pipeline was integrated into the operational workflow. New data is scanned continuously — each batch surfaces fresh clusters, and new members of already-flagged patterns are processed automatically. The tool improves over time through a feedback loop with domain experts.
Onboarding
Describe your columns. We scan every record. You get a report in five business days.
What you provide
- Your dataset
CSV file or database access — up to 1M records
- Column descriptions
Tell us what each column should contain — e.g. “director job titles”, “person first names”, “finance company names”
- 15-30 minute kickoff call
We align on what “bad data” means for your specific use case
What you receive
- Cluster Overview
Named groups of bad data — each with an AI-generated description, size, and example records
- Record Details
Every flagged record with its cluster, an AI explanation of what's wrong, and columns for your team's review
- 30-minute walkthrough call
We walk you through the findings, explain each cluster type, and discuss next steps
Built for teams that move data at scale
If you have text columns with millions of rows, you have broken records.
B2B Data Providers
You aggregate millions of contact records from dozens of sources. A LinkedIn scrape puts a person's name in the employer field. A vendor sends organization names in the first_name column. A job title field contains “01/2019 - Present” instead of an actual title. Those records ship to your customers, degrade their models, and trigger support escalations. Qualantic catches them before they leave your pipeline.
Also works for
HR & Recruiting Data
Job title normalization across millions of profiles. Date ranges, company names, and gibberish end up in the title field. Semantic analysis catches what standard validation can't.
Web Scraping Operations
Column shifts, parsing errors, field contamination. When a fraction of scraped records have wrong data in wrong columns, aggregate metrics will never show it.
Compliance & Governance
Regulatory reporting depends on record accuracy. Wrong entity types in screening systems or KYC fields with data from adjacent columns go beyond data quality.
Sound familiar?
Every team working with large text datasets hits the same wall.
Buying or acquiring text data?
You used to celebrate when a vendor delivered millions of records. Volume was the metric. But how many of those records actually make sense? A manual spot-check of 1,000 rows tells you almost nothing about the other 999,000. Qualantic scans the entire dataset and shows you exactly what you bought.
Already reviewing data manually?
Manual validation doesn't scale. You can review hundreds of records a day, maybe a thousand. Qualantic pre-screens your entire dataset and surfaces the clusters that need attention. Your team focuses on what matters instead of scrolling through rows.
Relying on rules and dashboards?
Your null checks pass. Your regex rules pass. Schema validation, freshness monitoring, format checks — all green. But inside those records, a job title says "01/2019 - Present" and a first name says "THC Pharmacy." Rule-based checks can't catch what's semantically wrong. Qualantic adds the layer your current tools are missing.
Pilot Data Scan
See what's hiding in your data. One scan, one report.
- One dataset, up to 3 text columns
- Up to 2M unique records
- Full anomaly report with clustered findings
- 30-min walkthrough call
- Full integration designs options
Need more? Need less? Let's talk about options on the call.
Ready to see what's hiding in your data?
Book a free 30-minute pilot call. We'll discuss your dataset and whether a scan makes sense.
Can't find a time that works? Email us