Eroding business trust
Every flawed report, faulty analysis, or broken ML model undermines the credibility of your entire data department. Stakeholders lose confidence and go back to deciding on intuition, which wastes your investment in analytics.
Data quality assurance. Guaranteed by SLA
Data without rigorous quality assurance is a liability. DoubleData provides a multi-layered QA process that turns raw, chaotic web data into clean, accurate data. Build your most important business decisions on data you can trust.
SLA-backed quality
Verified99.93%Accuracy
- 99.93%
- 99.9%
- every run
- sampled
We scrape data from over 5,000 sources
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The high, hidden cost of unreliable data
Eroding business trust
Wasted analyst and engineering hours
Flawed business decisions
Silent system failures
We scrape data and guarantee its quality
Quality is the foundation of our service. Our multi-stage QA process is built into every project from day one, so the data you receive is ready to use in your most important applications.
Data Security Management ISO/IEC 27001:2022Logical and business rule checks
On top of standard validation, we implement custom business rules that catch logical errors, such as a product price of $0.01 or an unrealistic delivery time. They remove errors that pass the structure check and still make no sense.
Manual "ground-truth" verification
Manual verification is our key differentiator. Our dedicated QA analysts take a sample of the data and manually compare it, field by field, against the source application. This is how we catch the subtle errors that automation can miss and deliver the highest possible accuracy.
Auto validation & schema enforcement
Automated validation is our first line of defense. Every dataset passes through automated tests that verify structural integrity, completeness (for example, row counts), and compliance with the predefined schema. Any anomalies are flagged automatically.
SLA-backed quality guarantee
We put our quality guarantee in the contract: our SLA includes specific, measurable metrics for data accuracy, completeness, and timeliness.
Frequently asked questions
We answer your key questions about our methodology, security, and the measurable impact of our work.
Our SLA is customized for each project, but standard metrics include field-level accuracy (for example, 99.93% of prices are correct), data completeness (for example, 99.9% of records contain all required fields), and timeliness of delivery (for example, data delivered on the agreed schedule).
This is part of our QA process. We use a hybrid approach, combining algorithmic matching (using unique identifiers like EANs or SKUs) with our proprietary machine learning models. These models match products on attributes like name, brand, and even the visual similarity of images, which matters most for unstructured data.
Automation is essential for scale, but it can miss subtle contextual errors. For example, an automated check might approve a product image that is actually a "404 Not Found" image. Our manual "ground-truth" verification is the final step and gives enterprise-class applications the data fidelity they need.
Our QA process is integrated with our scraper monitoring system. A change on a source site often triggers quality alerts (like a sudden drop in the number of records). Our engineering team gets notified right away and adapts the scraper, so quality issues are fixed before they reach your databases.
Achieve total data confidence
- Outline a clear path to reliable data.
- Pinpoint your core data quality issues.
- Quantify the business impact of bad data.