Frequently
Asked
Questions

Accuracy depends on the source portal and field type. For core structured fields — price, address, phone number, product title — accuracy is typically above 95%. We validate data against known patterns (phone number formats, postcode structures, price ranges) and flag anomalies before delivery. For geocoding, accuracy at building level is typically above 92% for urban addresses. We always provide a free sample so you can verify accuracy for your specific use case before committing.

Freshness depends on the pipeline schedule you choose. Daily pipeline runs capture data within 24 hours of the scheduled run time. Weekly runs deliver data updated within 7 days. For one-time extracts, we begin extraction on confirmation and deliver within 48–72 hours. We timestamp every record at the point of collection so you always know exactly when each data point was gathered.

Yes. When you request extraction from multiple sources (e.g. multiple property portals or business directories), we merge and deduplicate records using a combination of name, address, phone number, and URL matching. Duplicate confidence scores are included so you can review edge cases. Deduplication logic is transparent and customisable.

We monitor all active pipelines for extraction failures and schema changes. If a target website updates its layout, our engineering team rebuilds the affected extractors within 24–48 hours. For enterprise clients on SLA contracts, recovery time targets are contractually defined. You are notified proactively — we do not wait for you to discover a gap.

Yes. Standard normalisation (address parsing, phone number formatting, currency standardisation, category taxonomy mapping) is included in all projects. Custom normalisation — such as mapping source categories to your internal taxonomy, or standardising product names across multiple retailers — is available as an add-on and scoped during discovery.

Web scraping of publicly available data is generally legal in most jurisdictions. The landmark hiQ Labs v. LinkedIn ruling (US 9th Circuit) affirmed that scraping publicly accessible data does not violate the Computer Fraud and Abuse Act. We only collect data that is visible to any website visitor without authentication. We comply with applicable data protection laws including GDPR, India's DPDP Act, and CCPA. We recommend clients seek their own legal advice for specific use cases in regulated industries.

No. We only extract data that is publicly visible without logging in — the same information any member of the public can access through a standard browser. We do not attempt to bypass authentication, access private accounts, or circumvent security measures. This is a firm policy that applies to all projects.

We collect only publicly displayed data and do not collect sensitive personal data categories as defined under GDPR. For data involving individuals (such as business contact details visible in public directories), we advise clients on their own GDPR obligations as data controllers. Our EU clients typically use our data under legitimate interest grounds for B2B marketing. We are happy to review your specific use case with your DPO.

Yes. We review robots.txt directives for every target website and honour disallow rules for non-public sections. For public-facing data sections — product listings, business directories, property portals — robots.txt restrictions are assessed in the context of the legal and technical landscape. We do not scrape sections explicitly restricted to crawlers where the restriction applies to the specific data type.

Yes. We sign NDAs as standard for all client engagements involving proprietary briefings or data. Data Processing Agreements (DPAs) are available for clients operating under GDPR or similar frameworks. Contact us to request our standard DPA template or to negotiate a custom agreement.