AI Data Extraction
AI pipelines that read unstructured documents — invoices, contracts, medical records, applications — extract the fields you need, validate accuracy, and push clean structured data to your systems. At any volume.
Why Manual Data Entry Is Your Most Expensive Process
Every document that requires a human to read and re-type is a process bottleneck, an error source, and an invisible headcount cost.
Invoice approval cycles of 3–7 days. Contract review backlogs. Application processing queues. All driven by the speed at which humans can read and re-key unstructured documents.
Manual data entry carries a 1–5% human error rate. In invoicing, that's payment to wrong accounts. In medical records, that's clinical risk. In contracts, that's missed obligations.
More documents means more people processing them. There's no technology leverage. Every volume increase requires proportionally more manual processing headcount.
Manual entry gives you the extracted data but not the source. Who extracted it, when, from which version of the document — none of that is captured consistently.
AI extraction is not just faster than human data entry — it's more accurate. Because it never gets tired, never rushes, and applies the same validation logic to every document.
What AI Data Extraction Actually Does
A multi-stage pipeline — not OCR. Extraction, validation, cross-referencing, and structured output in one automated flow.
Document arrives via email attachment, upload portal, SFTP, or API. Any format: PDF, DOCX, image scan, or structured form.
AWS Textract or Claude Vision reads the document. Target fields extracted using AI-defined schema — not fixed templates.
Extracted data validated against business rules — totals checked, dates verified, mandatory fields confirmed, anomalies flagged.
Structured data pushed to your ERP, CRM, database, or spreadsheet — formatted exactly as your system expects.
Low-confidence extractions or validation failures route to a human review queue with the source document highlighted.
Not template-dependent. AI understands document structure and extracts the right fields even from varied layouts and formats.
PDFs, scanned images, Word documents, emails, and structured forms — all processed by the same pipeline.
Custom validation logic — totals cross-checked, date ranges validated, mandatory fields enforced — before data reaches your system.
Every extraction logged with source document reference, field-level confidence scores, and extraction timestamp. Fully auditable.
What Teams Use AI Data Extraction For
All use cases live in production. Metrics are 90-day averages.
Supplier invoices received by email → vendor, amount, line items, VAT, and due date extracted → matched against PO in ERP → approved automatically or flagged for review. Processing time: 4 seconds vs 8 minutes manually.
Legal contracts uploaded → key dates, obligations, termination clauses, and value extracted → obligation schedule created → pushed to CRM with alert triggers for renewal dates and milestone deadlines.
Loan, insurance, or job applications received in any format → personal details, financial data, and supporting documents extracted and validated → eligibility pre-check run → application record created and workflow triggered.
Identity documents, proof of address, and certifications extracted and cross-referenced against application data. Discrepancies flagged. Clean extractions auto-approved with immutable audit trail. Used in regulated finance and legal contexts.
AI Data Extraction Results
Aggregated from 70+ data extraction deployments. Measured 90 days post-launch.
Where AI Data Extraction Delivers Most
By document type, 90-day average across active clients.
Average ROI across all client types
Everything Included in AI Data Extraction
From schema design through to accuracy monitoring and exception handling.
Every workflow, configuration, and script is yours — with full documentation and Loom walkthroughs. Zero lock-in
Unlike template-based OCR, we use Claude's vision capabilities to understand document structure semantically — not pattern-matched against a fixed layout. An invoice from a new supplier in a format we've never seen is handled correctly because the AI understands what an invoice is, not just what it looks like.
We pre-process low-quality images with enhancement algorithms before extraction. For very poor scan quality (below 150 DPI), we route to human review with the enhanced image rather than force an extraction. Overall accuracy including poor-quality inputs: 97.8% across production deployments.
Extraction works natively in English, French, German, Spanish, Italian, Dutch, and Portuguese. Other languages are supported with additional fine-tuning. Language detection is automatic — you don't configure per document.
Yes. We've integrated with SAP, Oracle, NetSuite, Xero, QuickBooks, and several custom-built ERP and case management systems. If your system has an API or accepts structured file imports, we can push extracted data to it automatically.
Documents below the confidence threshold — or where validation rules fail — route to a human review queue. The reviewer sees the original document with the extracted fields highlighted. Corrections are logged and used to improve extraction accuracy in the next monthly calibration.
Only if you choose to use external APIs (like OpenAI or Anthropic). For sensitive documents, we can deploy the extraction model within your own cloud environment — AWS, Azure, or GCP — so document content never leaves your infrastructure. This is the default configuration for financial services and healthcare clients.
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