Your pipelines deliver records. We make sure they're right.
Scraped data is never perfect — formats break, fields go missing, and business logic changes without notice. Our automated validation engine checks every record against your rules (data types, value ranges, cross‑field consistency, schema compliance) and alerts you the moment something looks wrong. No more silent data corruption reaching your dashboards.
rules:
- field: "price"
checks: ["type:number", "> 0", "< 1000000"]
- field: "sku"
checks: ["non-empty", "unique"]
- cross_field: "sale_price <= list_price"
alert_on_failure: "slack:#data-quality"
12,400 records • 99.9% pass
Alert sent to #data-quality
Trusted data from leading platforms
Trust in your data starts with validation — not luck
Data validation is the automated safeguard that ensures every scraped record meets your quality standards before it enters your warehouse, dashboard, or operational system. Our engine checks each field against configurable rules — types, ranges, required presence, cross‑field logic, and business‑specific constraints — and takes action immediately: quarantining bad rows, sending alerts, or triggering auto‑correction. It’s the last line of defense between raw web chaos and trusted business data.
Whether you’re feeding a repricer that demands accurate prices, a CRM that requires complete lead profiles, or an analytics pipeline that breaks on unexpected nulls, our validation pipeline fits into your existing data flow — running silently in the background and speaking up only when attention is required.
- Validates data types, formats, value ranges, and required fields
- Checks cross‑field consistency and business logic rules
- Real‑time alerting via Slack, email, or webhook on failures
- Quarantine or auto‑correct records based on configurable rules
- Full audit trail and quality reports delivered on your schedule
Why bad data reaches your dashboards more often than you think
Without automated validation, errors slip through at every stage — from extraction to final report.
Silent data drift erodes trust
A website changes a field name from “price” to “Price” and suddenly your repricer is working with nulls. No error, no alert — just bad decisions for weeks before anyone notices.
Manual QA doesn't scale
Spot‑checking 200 records out of 100,000 gives a false sense of security. The errors you miss are the ones that break automated processes — and manual review can't catch them all.
Business rules live only in people's heads
An analyst knows sale_price should never exceed list_price, but that knowledge isn't codified. When the rule is broken, nobody finds out until a finance audit flags the discrepancy.
A validation engine that enforces your rules, every time
Every feature below is configured to your specific data model and business logic — no one‑size‑fits‑all templates.
Comprehensive rule library
Check data types, allowed values, ranges, regex patterns, uniqueness, required fields, cross‑field logic, and custom Python/SQL expressions. Mix and match to build your quality policy.
Intelligent failure handling
Quarantine failing records into a review table, auto‑correct using predefined repair rules, or simply log and alert. You choose the action per rule and severity.
Proactive alerting & dashboards
When validation rates drop or specific rules are breached, your team gets an alert in Slack or email. A real‑time dashboard shows quality trends across all data sources.
Audit trail & compliance
Every validation decision is logged — which record failed, which rule, and what action was taken. Full export for compliance, data governance, and internal audits.
{
"batch_id": "2026-07-22T08:00Z",
"records_checked": 12400,
"passed": 12392,
"quarantined": 8,
"top_fail_rule": "sale_price <= list_price"
}
From raw extract to validated, trustworthy data
Rulebook design
We work with your team to define the validation rules — data types, allowed ranges, required fields, and business logic — codifying your quality standards.
Pipeline configuration
Our engineers implement the rules in our validation engine, configure alerting channels and quarantine destinations, and run a test against a sample of your data.
Sample validation & review
A batch of your data is validated and a report delivered — you see exactly which records pass, which fail, and how the alerts behave before going live.
Production deployment & monitoring
Validation runs automatically on every data load. We monitor rule performance, alert your team on quality drops, and adapt rules when source schemas change.
Deliverables for every data validation project
Validation rulebook & quality policy
A document defining every rule, severity level, failure action, and alert channel — reviewed and approved by your data governance team.
Sample validation report
A batch of your data run through the engine, with a report showing pass rates per rule, quarantined records, and alert behaviour — for your validation and feedback.
Production pipeline + runbook
Validation live on your data feed. A runbook documents the rules, actions, alerting setup, and how to request rule changes.
Monthly quality health report
Summary of pass rates, failure trends, new rules added, and any source‑side changes that required rule adaptation — delivered proactively.
Flexible plans for every validation scope
One‑Time Data Audit
A comprehensive quality check against a single batch of data — ideal for a one‑off assessment of your current data health.
- Fixed scope & price
- Up to 20 validation rules
- Detailed audit report delivered
Managed Validation Feed
Ongoing validation of every data batch — we maintain the rules and monitoring, your data stays trustworthy automatically.
- Daily or per‑batch validation
- Unlimited rules & custom logic
- Alerts, dashboards & SLA
Enterprise Data Quality
For complex multi‑source environments, custom rule engines, private infrastructure, and dedicated quality engineers.
- Unlimited data sources & rules
- Dedicated quality engineering team
- SSO, audit logs, quarterly reviews
Teams where data errors are not an option
E‑commerce & Retail
Ensure every price, stock level, and product attribute is valid before it feeds your repricer or customer‑facing pages.
Finance & Investment
Validate alternative data streams — market prices, economic indicators, filing extracts — before they reach trading algorithms or risk models.
Healthcare & Pharma
Check drug pricing data, clinical trial metadata, and regulatory filings for completeness and accuracy before compliance reporting.
Business Intelligence
Guarantee that every dashboard and report is built on validated, trusted data — no more questioning the numbers in Monday meetings.
Data Validation vs. manual quality checks and home‑grown scripts
| Capability | Managed Data Validation | Manual Spot‑Checking | In‑House Validation Scripts |
|---|---|---|---|
| Comprehensive rule coverage (types, ranges, cross‑field, business logic) | |||
| Real‑time alerting on quality drops | |||
| Automated quarantine or correction | |||
| Audit trail & compliance logging | |||
| Scalable to millions of records | |||
| Ongoing rule maintenance & adaptation | Included | Your team | Your team |
“We finally trust the numbers in our dashboards — because we know every record was checked”
"We had a competitor price feed that would occasionally contain negative prices due to a site bug. The validation pipeline caught 100% of those before they hit our repricer. It saved us from pricing errors that could have cost thousands."
"The cross‑field validation is what we value most. A sale price greater than list price seems obvious — until it happens 50 times in a batch. Now we catch those inconsistencies before they reach our finance reports."
"We used to have one analyst spending 10 hours a week manually validating scraped data. Now the pipeline does it automatically every hour — and the quarantine table means they only review the records that actually need attention."
Validated data flows — and alerts fire — right into your existing stack
No new dashboards to learn unless you want one. Alerts and clean data land exactly where your team already works.
Get a fixed‑price quote for your data validation project
Share a sample of your data and the rules you care about — we'll return a validation report and a fixed‑price estimate within two business days.
Common questions about Data Validation
Your data, validated against your rules — starting with a free sample
Send us a sample of your scraped data and the rules you care about. We'll return a validation report within two business days — no commitment, no sales pitch.
Most validation pipelines deliver the first quality report within 2–3 weeks of kickoff.
