Automated document extraction and routing using Google Cloud Document AI and Gemini Vision. Processes contracts, invoices, financial statements, and compliance documents at scale — turning hours of manual work into seconds.
We implement a fully managed document processing pipeline on Google Cloud — ingesting documents from email, cloud storage, or API upload, extracting structured data using specialised Document AI processors, validating with Gemini Vision, and delivering clean data to BigQuery and your downstream systems. The pipeline runs continuously with no manual intervention for high-confidence documents.
Staff spend hours manually reading and re-typing data from PDFs, invoices, and contracts into systems — a slow, expensive, and error-prone process at scale.
Human data entry introduces transcription errors that compound downstream — causing incorrect payments, compliance failures, and costly corrections.
As document volumes grow, review queues build up, delaying procurement approvals, invoice processing, and contract execution by days or weeks.
Trained processors for invoices, contracts, purchase orders, financial statements, and compliance forms — each with document-type-specific field extraction.
Gemini 2.0 Flash validates extracted fields in context — catching misreads, layout anomalies, and inconsistencies that rule-based OCR misses.
Every extracted field carries a confidence score. Low-confidence fields are flagged automatically for human review rather than processed blindly.
Documents failing confidence thresholds are routed to the appropriate reviewer via Cloud Workflows, with extracted data pre-populated for efficient review.
All extracted data lands directly in BigQuery, enabling analytics, trend reporting, and downstream system integration via standard APIs.
Google Cloud Document AI provides pre-trained processors for invoices, receipts, contracts, identity documents, lending documents, and tax forms. Custom processors can be trained for organisation-specific document types with as few as 50 labelled examples. During implementation we configure the processor mix based on your actual document portfolio.
Each extracted field carries a confidence score from 0 to 1. You define the thresholds during implementation — for example, fields below 0.85 confidence are flagged, fields below 0.60 are routed to human review. Reviewers see the original document alongside the extracted data, making corrections fast. Corrected data can feed back into model fine-tuning over time.
Document AI's handwriting processor handles printed handwriting reliably. Heavily stylised or cursive handwriting has lower accuracy than typed text, so those documents are typically routed to a human review queue after initial extraction. Gemini Vision is used as a second pass for ambiguous fields, improving overall accuracy above what OCR alone achieves.
Yes. Extracted data lands in BigQuery and can be pushed to downstream systems via Cloud Workflows, Pub/Sub, or direct API calls. We implement standard integrations with SAP, Oracle, NetSuite, Xero, and other ERPs during the engagement. Custom webhook delivery is also available for any system with an API.
Stop paying people to copy data from documents. We implement a production-grade Document AI pipeline in 3 weeks.