What specific quality metrics do you guarantee in your SLA?

Our SLA is customized for each project, but standard metrics include Field-Level Accuracy (for example, 99.5% of prices are correct), Data Completeness (for example, 99.9% of records contain all required fields), and Timeliness of Delivery (for example, data delivered within X hours of the agreed schedule).

How do you handle data matching and deduplication?

This is part of our advanced 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 can match products based on attributes like name, brand, and even visual similarity of images, which is crucial for unstructured data.

Isn't automated validation enough? Why is manual verification necessary?

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 that ensures the highest data fidelity, which is absolutely critical for enterprise-class applications.

How does your QA process react to changes on a source website?

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). This immediately notifies our engineering team to adapt the scraper, ensuring that quality issues are resolved proactively, before they have a chance to corrupt your databases.

Data quality assurance. Guaranteed by SLA

Data without rigorous quality assurance is not an asset; it is a liability. DoubleData provides a multi-layered QA process that transforms raw, chaotic web data into a clean, accurate, and reliable resource. Build your most critical business decisions on a foundation of absolute trust.

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SLA-backed quality

verified

99.5%accuracy

Field-level accuracy99.5%
Data completeness99.9%
Schema validationevery run
Manual spot-checksampled

We scrape data from over 5 000 sources

PROBLEMS

The high, hidden cost of unreliable data

Inaccurate, incomplete, or inconsistent data is not just a technical problem. It is a silent killer of business strategies that erodes trust, wastes resources, and leads to costly decision-making errors.

Eroding business trust

Every flawed report, faulty analysis, or broken ML model undermines the credibility of your entire data department. Stakeholders lose confidence and revert to making decisions based on intuition, not data, which nullifies your investment in analytics.

Wasted analyst and engineering hours

Your analysts and data scientists spend most of their time cleaning and verifying data instead of analyzing and modeling it. This is a massive, hidden operational cost and a major source of frustration for your top talent.

Flawed business decisions

Pricing strategies based on inaccurate competitor data. Sales teams calling outdated leads. Marketing campaigns targeting the wrong segments. Poor data quality leads directly to bad outcomes and lost revenue.

Silent system failures

Inconsistent data formats (like dates, currencies, or opening hours) cause silent failures in automated systems and ETL pipelines. These errors often go undetected for weeks, quietly corrupting your databases.

SOLUTIONS

We don't just scrape data. We guarantee its quality

For us, quality is not an add-on. It is the foundation of our service. Our multi-stage QA process is built into every project from the very beginning to ensure you receive data that is ready for immediate use in your most critical applications.

Data Security Management ISO/IEC 27001

Logical and business rule checks

We go a step beyond standard validation. We implement custom business rules that catch logical errors, such as a product price of $0.01 or an unrealistic delivery time. This allows us to eliminate errors that are structurally correct but contextually meaningless.

Manual "ground-truth" verification

This is our key differentiator. Our dedicated QA analysts take a sample of the data and manually compare it, field by field, against the live source application. This is the only way to guarantee the highest possible accuracy and catch the subtle errors that automation cannot see.

Auto validation & schema enforcement

This 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 instantly.

SLA-backed quality guarantee

We do not just promise quality; we guarantee it in our contract. Our SLA includes specific, measurable metrics for data accuracy, completeness, and timeliness. We take full responsibility for the quality of what we deliver.

Competitor visibility that accelerated growth

Our tailored scraping and data enrichment services allowed this Central and Eastern European qCommerce company to access critical competitor and market information. This facilitated informed decision-making and enabled them to refine their strategies, ultimately leading to accelerated growth and an enhanced competitive edge.

qCommerce

CEE Leader

Dynamic pricing built on competitor data

We provided this Central and Eastern European online grocery company with extensive scraping services, which allowed them to gather essential pricing and product data from competitors. This information helped them create a dynamic pricing strategy, resulting in increased sales and a stronger market presence.

Online Grocery

CEE Leader

Event and pricing data that grew market share

By leveraging our scraping services, this European ticket online sales company gained access to critical event and pricing data. This enabled them to refine their offerings, provide a more seamless user experience, and ultimately grow their market share in the competitive ticketing industry.

Online Ticketing

European Leader

Real-time restaurant data behind a better platform

By utilizing our comprehensive scraping services, this major European food delivery company gained access to valuable, real-time restaurant and menu data. This enabled them to optimize their platform and improve the user experience, resulting in increased customer satisfaction and revenue growth.

Food Delivery

European leader

FAQ

Frequently Asked Questions

We believe in transparency. Here are the answers to your key questions about our methodology, security, and the measurable impact we deliver.

Achieve total data confidence

Take the first step. Schedule a complimentary consultation to assess your data quality challenges and outline a path to trusted, actionable insights.

Outline a clear path to reliable data.
Pinpoint your core data quality issues.
Quantify the business impact of bad data.

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