Question 1

How do you bypass advanced anti-bot systems to ensure reliable, high-frequency data collection?

Accepted Answer

This is the core of what we do. We've spent over a decade successfully getting data from the toughest sources, including banking and finance sites. We’re successful because we don’t rely on off-the-shelf tools; we built our own infrastructure from the ground up specifically to defeat these systems at scale. There's no single magic bullet. Instead, our success comes from several systems working together: Intelligent Proxy & IP Management: We run our own global network of residential, ISP, and mobile proxies. Our platform automatically rotates IPs, mimics human Browse patterns, and uses geo-targeting to get around IP-based blocking and rate limits, even on the most difficult websites. Advanced Anti-Bot & CAPTCHA Solvers: We use a fleet of smart, headless browsers that render JavaScript-heavy sites exactly as a normal browser would. These are equipped with our own AI models trained to automatically identify and solve all types of CAPTCHA challenges, ensuring the data collection is never interrupted. Resilience to Structural Changes: Our platform constantly monitors source websites for layout or structural changes. Our parsers are designed to be resilient, but if a site change does impact data collection, our system creates an automatic alert. Our engineering team is then responsible for adapting the logic, with resolution times guaranteed by our SLA. Our central platform coordinates all of these systems. This integrated approach is how we reliably pull data from sources that block others, freeing up your team to focus on insights, not the complexities of data acquisition.

Question 2

How does your system stay reliable and handle huge amounts of data? What’s your uptime guarantee?

Accepted Answer

Our entire system is built fully in-house with our own proprietary scraping infrastructure on top of global cloud platforms. It was designed from day one to handle huge, unpredictable workloads. We run a decoupled microservices architecture on Kubernetes. This approach eliminates single points of failure and guarantees high availability. Because each service is containerized and independent, we can perform rolling updates for any component with zero system-wide downtime. Our standard promise is 99.9% uptime of Doubledata systems that orchiestrate scrapers execution. For us, that's the foundation of a good partnership.

Question 3

Can you get data from mobile apps? How do you deal with security like certificate pinning?

Accepted Answer

Yes, absolutely. We have a lot of experience getting data from native iOS and Android apps. It’s much more difficult than standard web scraping, and we are very good at it. Mobile apps are designed with extra security to prevent exactly this. Our team uses advanced techniques to understand how the app communicates with its servers. We then build systems that can talk to the app’s API in a way that is identical to a real phone. This allows us to get around roadblocks like certificate pinning and pull the clean data directly from the source.

Question 4

How do we get the data from you? Is it an API? Do you have webhooks or pre-built connectors?

Accepted Answer

Getting data into your system should be the easy part. We offer a few simple ways to do it. You can pull data from our API whenever you need it, or you can have us ping your system with a webhook the moment a new dataset is ready. We can even drop the files directly into your cloud storage bucket (like Amazon S3 or Google Cloud). The data comes in standard formats like JSON or CSV. If you’re working with massive datasets, we can also provide it in high-performance formats like Parquet. And yes, we have ready-to-go connectors for the tools you already use, including Snowflake, BigQuery, Tableau, and Power BI. The goal is to get you working with the data immediately, with almost no setup required.

Question 5

How do you make sure the data is accurate?

Accepted Answer

We're obsessed with data quality because we know that bad data is useless. Our entire QA process is built on two principles: powerful automated checks followed by expert human verification. 1. Automated QA checks First, every dataset passes through a series of automated checks. Their job is to validate the data's structural integrity and logical consistency by looking for common issues: Checking for correct formatting against the established template. Verifying data completeness, for example, by comparing row counts to previous runs. Flagging logical anomalies, like a price or fee that falls completely outside of a predefined reasonable range. 2. Manual "ground-truth" verification Automation is crucial, but it's the manual verification by our QA analysts that ensures true accuracy. This is more than a simple validation; it’s a rigorous spot-check of the data. Our analysts take a data sample and compare it field-by-field against the live source application. This manual "ground-truth" check guarantees the data we deliver is an exact match for what a real user sees, catching the subtle errors that automated systems can miss. If a discrepancy is found, no matter how small, it is formally documented, assigned a trackable issue link, and escalated to our engineering team for resolution. This is how we guarantee the highest possible level of accuracy.

Total Cost of Ownership (TCO)
High	Very High
Internal Team Workload
Low	Very High
Reliability & Guarantees (SLA)
Very High	Medium
Scalability & Performance
Very High	High
Time-to-Value
Medium	Very Low
Ideal For
Enterprises focused on high-volume data outcomes, not operations.	Enterprises requiring total process ownership.
Data Quality & Accuracy
Very High	High
Support & Partnership
Very High	N/A
Legal & Compliance
Risks imposed on data provider	Full responsibility for legal aspects

Features	Dedicated Scraping Services	In-House Scraping Team	API-based Scraping	No-Code / Low-Code Tools	Off-the-Shelf Datasets
Total Cost of Ownership (TCO)	High	Very High	Medium	Low	Very Low
Internal Team Workload	Low	Very High	High	Medium	Very Low
Reliability & Guarantees (SLA)	Very High	Medium	High	Very Low	N/A
Scalability & Performance	Very High	High	High	Low	N/A
Time-to-Value	Medium	Very Low	High	Very High	Very High
Ideal For	Enterprises focused on high-volume data outcomes, not operations.	Enterprises requiring total process ownership.	Developers & tech companies.	Non-developers, marketers, & startups.	Users needing instant, standard datasets.
Data Quality & Accuracy	Very High	High	Medium	Low	Medium
Support & Partnership	Very High	N/A	Low	Very Low	Very Low
Legal & Compliance	Risks imposed on data provider	Full responsibility for legal aspects	Full responsibility for legal aspects	Full responsibility for legal aspects	Risks imposed on 3rd party

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01How do you bypass advanced anti-bot systems to ensure reliable, high-frequency data collection?

02How does your system stay reliable and handle huge amounts of data? What’s your uptime guarantee?

03Can you get data from mobile apps? How do you deal with security like certificate pinning?

04How do we get the data from you? Is it an API? Do you have webhooks or pre-built connectors?

05How do you make sure the data is accurate?

01How do you ensure legal and GDPR compliance?

02How is the service priced?

03What does the onboarding process look like and how long does it take?

04What if I need a new data source or a different format later?

05What does your technical support model look like?

01What exactly does the onboarding process look like, and how much of my team's time will it require?

02How do you guarantee the data is accurate? What happens when a competitor's website changes?

03How does your pricing model work? What does the price depend on and are there any hidden costs?

04Is web scraping legal, and how do you handle security and compliance with regulations like GDPR?

05My needs are very specific. How "non-standard" can the data you collect be?

01Is scraping 100% legal? How do you protect me and my company from legal risk?

02What does the onboarding process look like, and can I test the service before signing a long-term contract (Proof of Concept)?

03How do you guarantee data quality and accurate product matching? Many companies struggle with this.

04What does your pricing model look like? What determines the final price?

05What happens when a competitor's site changes and the data stops? Who is responsible and how fast do you react?

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