Turn Raw Data Into
Clean, Actionable Records.

Structured data from websites, directories, property listings, company databases and online marketplaces; unstructured data from PDFs, Word documents, Excel files and images; and data from APIs where you have access credentials. Common use cases include lead enrichment, competitor monitoring, supplier data aggregation, document processing and CRM population from offline or legacy sources.

Yes. We use AI models to extract text, tables and structured fields from PDFs, including scanned or photographed documents. Accuracy is typically very high for clean digital PDFs and generally strong for scanned documents, depending on image quality. We always validate accuracy on a sample batch before running a full extraction, and we report accuracy rates so you can decide whether to add a human review step.

For clean digital documents and well-structured web pages, accuracy is typically in the 95 to 99 percent range. Scanned or handwritten documents are more variable but still viable, particularly when combined with a validation layer. We test against real samples first and configure confidence thresholds so that low-confidence extractions are flagged for review rather than pushed through automatically.

Yes. We connect the extraction pipeline directly to your CRM via API, so data flows in automatically without manual imports. We have integrated with HubSpot, Pipedrive, Salesforce, Zoho and most other major CRMs, as well as custom databases, Airtable and Google Sheets. We confirm compatibility during the scoping session.

Both are common. A one-off extraction cleans up and imports historical data, for example populating a new CRM from an old spreadsheet or a competitor list. An ongoing feed runs on a schedule or trigger, keeping your records updated as new data becomes available. Many clients start with a one-off migration, then add a recurring feed once they have seen the output quality.